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Sample records for iterative learning controller

  1. An iterative learning controller for nonholonomic mobile robots

    SciTech Connect

    Oriolo, G.; Panzieri, S.; Ulivi, G.

    1998-09-01

    The authors present an iterative learning controller that applies to nonholonomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained-form. The learning algorithm exploits the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method.

  2. Iterative learning control algorithm for spiking behavior of neuron model

    NASA Astrophysics Data System (ADS)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  3. Fast calculation of the `ILC norm' in iterative learning control

    NASA Astrophysics Data System (ADS)

    Rice, Justin K.; van Wingerden, Jan-Willem

    2013-06-01

    In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

  4. Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network.

    PubMed

    Wang, Ying-Chung; Chien, Chiang-Ju; Teng, Ching-Cheng

    2004-06-01

    In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the given ORFNN to approximate an optimal equivalent controller. Some auxiliary control components are applied to eliminate approximation error and ensure learning convergence. Since the optimal ORFNN parameters for a best approximation are generally unavailable, an adaptive algorithm with projection mechanism is derived to update all the consequent, premise, and recurrent parameters during iteration processes. Only one network is required to design the ORFNN-based DAILC and the plant nonlinearities, especially the nonlinear input gain, are allowed to be totally unknown. Based on a Lyapunov-like analysis, we show that all adjustable parameters and internal signals remain bounded for all iterations. Furthermore, the norm of state tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity. Finally, iterative learning control of two nonlinear systems, inverted pendulum system and Chua's chaotic circuit, are performed to verify the tracking performance of the proposed learning scheme.

  5. Further results on iterative learning control with convergence conditions for linear time-variant discrete systems

    NASA Astrophysics Data System (ADS)

    Li, Xiao-Dong; Ho, John K. L.

    2011-06-01

    This article is concerned with some further results on iterative learning control (ILC) algorithms with convergence conditions for linear time-variant discrete systems. By converting two-Dimensional (2-D) ILC process of the linear time-variant discrete systems into 1-D linear time-invariant discrete systems, this article presents convergent ILC algorithms with necessary and sufficient conditions for two classes of linear time-variant discrete systems. Main results in (Li, X.-D., Ho, J.K.L., and Chow, T.W.S. (2005), 'Iterative Learning Control for Linear Time-variant Discrete Systems Based on 2-D System Theory', IEE Proceedings, Control Theory and Applications, 152, 13-18 and Huang, S.N., Tan, K.K., and Lee, T.H. (2002), 'Necessary and Sufficient Condition for Convergence of Iterative Learning Algorithm', Automatica 38, 1257-1260) are extended and generalised.

  6. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  7. Implementation of a new iterative learning control algorithm on real data.

    PubMed

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome. PMID:26931852

  8. Implementation of a new iterative learning control algorithm on real data.

    PubMed

    Zamanian, Hamed; Koohi, Ardavan

    2016-02-01

    In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

  9. Control of a pneumatic power active lower-limb orthosis with filter-based iterative learning control

    NASA Astrophysics Data System (ADS)

    Huang, Chia-En; Chen, Jian-Shiang

    2014-05-01

    A filter-based iterative learning control (FILC) scheme is developed in this paper, which consists in a proportional-derivative (PD) feedback controller and a feedforward filter. Moreover, based on two-dimensional system theory, the stability of the FILC system is proven. The design criteria for a wavelet transform filter (WTF) - chosen as the feedforward filter - and the PD feedback controller are also given. Finally, using a pneumatic power active lower-limb orthosis (PPALO) as the controlled plant, the wavelet-based iterative learning control (WILC) implementation and the orchestration of a trajectory tracking control simulation are given in detail and the overall tracking performance is validated.

  10. Probabilistic tracking control for non-Gaussian stochastic process using novel iterative learning algorithms

    NASA Astrophysics Data System (ADS)

    Yi, Yang; Sun, ChangYin; Guo, Lei

    2013-07-01

    A new generalised iterative learning algorithm is presented for complex dynamic non-Gaussian stochastic processes. After designed neural networks are used to approximate the output probability density function (PDF) of the stochastic system in the repetitive processes or the batch processes, the complex probabilistic tracking control to the output PDF is simplified into a parameter tuning problem between two adjacent repetitive processes. Under this framework, this article studies a novel model free iterative learning control problem and proposes a convex optimisation algorithm based on a set of designed linear matrix inequalities and L 1 optimisation index. It is noted that such an algorithm can improve the tracking performance and robustness for the closed-loop PDF control. A simulated example is given, which effectively demonstrates the use of the proposed control algorithm.

  11. Enhanced iterative learning control for a piezoelectric actuator system using wavelet transform filtering

    NASA Astrophysics Data System (ADS)

    Chien, Chiang-Ju; Lee, Fu-Shin; Wang, Jhen-Cheng

    2007-01-01

    For trajectory tracking of a piezoelectric actuator system, an enhanced iterative learning control (ILC) scheme based on wavelet transform filtering (WTF) is proposed in this research. The enhanced ILC scheme incorporates a state compensation in the ILC formula. Combining state compensation with iterative learning, the scheme enhances tracking accuracies substantially, in comparison to the conventional D-type ILC and a proportional control-aided D-type ILC. The wavelet transform is adopted to filter learnable tracking errors without phase shift. Based on both a time-frequency analysis of tracking errors and a convergence bandwidth analysis of ILC, a two-level WTF is chosen for ILC in this study. The enhanced ILC scheme using WTF was applied to track two desired trajectories, one with a single frequency and the other with multiple frequencies, respectively. Experimental results validate the efficacy of the enhanced ILC in terms of the speed of convergence and the level of long-term tracking errors.

  12. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  13. Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

    NASA Astrophysics Data System (ADS)

    Li, Zhifu; Hu, Yueming; Li, Di

    2016-08-01

    For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

  14. Iterative learning control with applications in energy generation, lasers and health care

    PubMed Central

    Tutty, O. R.

    2016-01-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

  15. Iterative learning control with applications in energy generation, lasers and health care

    NASA Astrophysics Data System (ADS)

    Rogers, E.; Tutty, O. R.

    2016-09-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

  16. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  17. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.

    PubMed

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

    2015-02-01

    A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment.

  18. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.

    PubMed

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

    2015-02-01

    A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment. PMID:26052360

  19. Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-10-01

    Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

  20. Adaptive iterative learning control for nonlinearly parameterised systems with unknown time-varying delays and input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Chi, Ronghu; Ji, Honghai

    2015-06-01

    In this work, an adaptive iterative learning control (AILC) scheme is proposed to address a class of nonlinearly parameterised systems with both unknown time-varying delays and input saturations. By incorporating a saturation function, a novel iterative learning control mechanism is constructed with a feedback term in the time domain and a fully saturated adaptive learning term in the iteration domain, which is used to estimate the unknown time-varying system uncertainty. A new time-weighted Lyapunov-Krasovskii-like composite energy function (LKL-CEF) is designed for the convergence analysis where time-weighted inputs, states and estimates of system uncertainty are all considered. Despite the existence of time-varying parametric uncertainties, time-varying delays, input saturations and local Lipschitz nonlinearities, the learning convergence is guaranteed with rigorous mathematical analysis. Simulation results verify the correctness and effectiveness of the proposed method further.

  1. Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties

    NASA Astrophysics Data System (ADS)

    Hoa Nguyen, Dinh; Banjerdpongchai, David

    2010-12-01

    This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

  2. Gait simulation via a 6-DOF parallel robot with iterative learning control.

    PubMed

    Aubin, Patrick M; Cowley, Matthew S; Ledoux, William R

    2008-03-01

    We have developed a robotic gait simulator (RGS) by leveraging a 6-degree of freedom parallel robot, with the goal of overcoming three significant challenges of gait simulation, including: 1) operating at near physiologically correct velocities; 2) inputting full scale ground reaction forces; and 3) simulating motion in all three planes (sagittal, coronal and transverse). The robot will eventually be employed with cadaveric specimens, but as a means of exploring the capability of the system, we have first used it with a prosthetic foot. Gait data were recorded from one transtibial amputee using a motion analysis system and force plate. Using the same prosthetic foot as the subject, the RGS accurately reproduced the recorded kinematics and kinetics and the appropriate vertical ground reaction force was realized with a proportional iterative learning controller. After six gait iterations the controller reduced the root mean square (RMS) error between the simulated and in situ; vertical ground reaction force to 35 N during a 1.5 s simulation of the stance phase of gait with a prosthetic foot. This paper addresses the design, methodology and validation of the novel RGS. PMID:18334421

  3. Gait simulation via a 6-DOF parallel robot with iterative learning control.

    PubMed

    Aubin, Patrick M; Cowley, Matthew S; Ledoux, William R

    2008-03-01

    We have developed a robotic gait simulator (RGS) by leveraging a 6-degree of freedom parallel robot, with the goal of overcoming three significant challenges of gait simulation, including: 1) operating at near physiologically correct velocities; 2) inputting full scale ground reaction forces; and 3) simulating motion in all three planes (sagittal, coronal and transverse). The robot will eventually be employed with cadaveric specimens, but as a means of exploring the capability of the system, we have first used it with a prosthetic foot. Gait data were recorded from one transtibial amputee using a motion analysis system and force plate. Using the same prosthetic foot as the subject, the RGS accurately reproduced the recorded kinematics and kinetics and the appropriate vertical ground reaction force was realized with a proportional iterative learning controller. After six gait iterations the controller reduced the root mean square (RMS) error between the simulated and in situ; vertical ground reaction force to 35 N during a 1.5 s simulation of the stance phase of gait with a prosthetic foot. This paper addresses the design, methodology and validation of the novel RGS.

  4. Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach

    NASA Astrophysics Data System (ADS)

    Smolders, K.; Volckaert, M.; Swevers, J.

    2008-11-01

    This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.

  5. Use of PID and Iterative Learning Controls on Improving Intra-Oral Hydraulic Loading System of Dental Implants

    NASA Astrophysics Data System (ADS)

    Huang, Yi-Cheng; Chan, Manuel; Hsin, Yi-Ping; Ko, Ching-Chang

    This study presents the control design and tests of an intra-oral hydraulic system for quantitatively loading of a dental implant. The computer-controlled system was developed and employed for better pressure error compensation by PID (proportional-integral-derivative) control and point-to-point iterative learning algorithm. In vitro experiments showed that implant loading is precisely controlled (error 3%) for 0.5Hz loading without air inclusion, and reasonably performed (error<10%) with air inclusion up to 20% of the total hydraulic volume. The PID controller maintains forces at the desired level while the learning controller eliminates overshoot/undershoot at the onset of each loading cycle. The system can be potentially used for in vivo animal studies for better understanding of how bone responds to implant loading. Quantitative information derived from this biomechanical model will add to improved designs of dental implants.

  6. Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

    PubMed

    Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

    2015-11-01

    This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

  7. Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

    PubMed

    Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

    2016-02-01

    Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

  8. Coordinates transformation and learning control for visually-guided voluntary movement with iteration: a Newton-like method in a function space.

    PubMed

    Kawato, M; Isobe, M; Maeda, Y; Suzuki, R

    1988-01-01

    In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n + 1)-th iteration is a sum of the motor command in the n-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in the n-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes. PMID:3179342

  9. Coordinates transformation and learning control for visually-guided voluntary movement with iteration: a Newton-like method in a function space.

    PubMed

    Kawato, M; Isobe, M; Maeda, Y; Suzuki, R

    1988-01-01

    In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n + 1)-th iteration is a sum of the motor command in the n-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in the n-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes.

  10. ITER Plasma Control System Development

    NASA Astrophysics Data System (ADS)

    Snipes, Joseph; ITER PCS Design Team

    2015-11-01

    The development of the ITER Plasma Control System (PCS) continues with the preliminary design phase for 1st plasma and early plasma operation in H/He up to Ip = 15 MA in L-mode. The design is being developed through a contract between the ITER Organization and a consortium of plasma control experts from EU and US fusion laboratories, which is expected to be completed in time for a design review at the end of 2016. This design phase concentrates on breakdown including early ECH power and magnetic control of the poloidal field null, plasma current, shape, and position. Basic kinetic control of the heating (ECH, ICH, NBI) and fueling systems is also included. Disruption prediction, mitigation, and maintaining stable operation are also included because of the high magnetic and kinetic stored energy present already for early plasma operation. Support functions for error field topology and equilibrium reconstruction are also required. All of the control functions also must be integrated into an architecture that will be capable of the required complexity of all ITER scenarios. A database is also being developed to collect and manage PCS functional requirements from operational scenarios that were defined in the Conceptual Design with links to proposed event handling strategies and control algorithms for initial basic control functions. A brief status of the PCS development will be presented together with a proposed schedule for design phases up to DT operation.

  11. Iterative learning control for synchronization of reticle stage and wafer stage in step-and-scan lithographic equipment

    NASA Astrophysics Data System (ADS)

    Li, Lan-lan; Hu, Song; Zhao, Li-xin; Ma, Ping

    2013-08-01

    Lithographic equipments are highly complex machines used to manufacture integrated circuits (ICs). To make larger ICs, a larger lens is required, which, however, is prohibitively expensive. The solution to this problem is to expose a chip not in one flash but in a scanning fashion. For step-and-scan lithographic equipment (wafer scanner), the image quality is decided by many factors, in which synchronization of reticle stage and wafer stage during exposure is a key one. In this paper, the principle of reticle stage and wafer stage was analyzed through investigating the structure of scanners, firstly. While scanning, the reticle stage and wafer stage should scan simultaneously at a high speed and the speed ratio is 1:4. Secondly, an iterative learning controller (ILC) for synchronization of reticle stage and wafer stage is presented. In the controller, a master-slave structure is used, with the wafer stage acting as the master, and the reticle stage as the slave. Since the scanning process of scanner is repetitive, ILC is used to improve tracking performance. A simple design procedure is presented which allows design of the ILC system for the reticle stage and wafer stage independently. Finally, performance of the algorithm is illustrated by simulated on the virtual stages (the reticle stage and wafer stage).The results of simulation experiments and theory analyzing demonstrate that using the proposed controller better synchronization performance can be obtained for the reticle stage and wafer stage in scanner. Theory analysis and experiment shows the method is reasonable and efficient.

  12. Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

    NASA Astrophysics Data System (ADS)

    Blackwell, Mark W.; Tutty, Owen R.; Rogers, Eric; Sandberg, Richard D.

    2016-01-01

    The inclusion of smart devices in wind turbine rotor blades could, in conjunction with collective and individual pitch control, improve the aerodynamic performance of the rotors. This is currently an active area of research with the primary objective of reducing the fatigue loads but mitigating the effects of extreme loads is also of interest. The aerodynamic loads on a wind turbine blade contain periodic and non-periodic components and one approach is to consider the application of iterative learning control algorithms. In this paper, the control design is based on a simple, in relative terms, computational fluid dynamics model that uses non-linear wake effects to represent flow past an airfoil. A representation for the actuator dynamics is included to undertake a detailed investigation into the level of control possible and on how performance can be effectively measured.

  13. Learning to improve iterative repair scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene

    1992-01-01

    This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

  14. Novel aspects of plasma control in ITER

    NASA Astrophysics Data System (ADS)

    Humphreys, D.; Ambrosino, G.; de Vries, P.; Felici, F.; Kim, S. H.; Jackson, G.; Kallenbach, A.; Kolemen, E.; Lister, J.; Moreau, D.; Pironti, A.; Raupp, G.; Sauter, O.; Schuster, E.; Snipes, J.; Treutterer, W.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

    2015-02-01

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  15. Novel aspects of plasma control in ITER

    SciTech Connect

    Humphreys, D.; Jackson, G.; Walker, M.; Welander, A.; Ambrosino, G.; Pironti, A.; Felici, F.; Kallenbach, A.; Raupp, G.; Treutterer, W.; Kolemen, E.; Lister, J.; Sauter, O.; Moreau, D.; Schuster, E.

    2015-02-15

    ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

  16. Kernel-based least squares policy iteration for reinforcement learning.

    PubMed

    Xu, Xin; Hu, Dewen; Lu, Xicheng

    2007-07-01

    In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating

  17. Iter

    NASA Astrophysics Data System (ADS)

    Iotti, Robert

    2015-04-01

    ITER is an international experimental facility being built by seven Parties to demonstrate the long term potential of fusion energy. The ITER Joint Implementation Agreement (JIA) defines the structure and governance model of such cooperation. There are a number of necessary conditions for such international projects to be successful: a complete design, strong systems engineering working with an agreed set of requirements, an experienced organization with systems and plans in place to manage the project, a cost estimate backed by industry, and someone in charge. Unfortunately for ITER many of these conditions were not present. The paper discusses the priorities in the JIA which led to setting up the project with a Central Integrating Organization (IO) in Cadarache, France as the ITER HQ, and seven Domestic Agencies (DAs) located in the countries of the Parties, responsible for delivering 90%+ of the project hardware as Contributions-in-Kind and also financial contributions to the IO, as ``Contributions-in-Cash.'' Theoretically the Director General (DG) is responsible for everything. In practice the DG does not have the power to control the work of the DAs, and there is not an effective management structure enabling the IO and the DAs to arbitrate disputes, so the project is not really managed, but is a loose collaboration of competing interests. Any DA can effectively block a decision reached by the DG. Inefficiencies in completing design while setting up a competent organization from scratch contributed to the delays and cost increases during the initial few years. So did the fact that the original estimate was not developed from industry input. Unforeseen inflation and market demand on certain commodities/materials further exacerbated the cost increases. Since then, improvements are debatable. Does this mean that the governance model of ITER is a wrong model for international scientific cooperation? I do not believe so. Had the necessary conditions for success

  18. Eliminating Unpredictable Variation through Iterated Learning

    ERIC Educational Resources Information Center

    Smith, Kenny; Wonnacott, Elizabeth

    2010-01-01

    Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

  19. Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data

    PubMed Central

    Li, Fengqi; Yu, Chuang; Yang, Nanhai; Xia, Feng; Li, Guangming

    2013-01-01

    Transductive graph-based semisupervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices. Those methods propagate label information of labeled samples to neighbors through their edges in order to get the predicted labels of unlabeled samples. Most popular semi-supervised learning approaches are sensitive to initial label distribution which happened in imbalanced labeled datasets. The class boundary will be severely skewed by the majority classes in an imbalanced classification. In this paper, we proposed a simple and effective approach to alleviate the unfavorable influence of imbalance problem by iteratively selecting a few unlabeled samples and adding them into the minority classes to form a balanced labeled dataset for the learning methods afterwards. The experiments on UCI datasets and MNIST handwritten digits dataset showed that the proposed approach outperforms other existing state-of-art methods. PMID:23935439

  20. Accelerate OPC convergence with new iteration control methodology

    NASA Astrophysics Data System (ADS)

    Wang, Ching-Heng; Liu, Qingwei; Zhang, Liguo

    2008-10-01

    A dilemmatic trade-off that all OPC engineers are facing everyday is the convergence of the OPC result and the control of the OPC iteration times. Theoretically, infinite times of OPC iterations are needed to achieve a convergent and stable correction result. But actually there should always be a cut-off for the iteration time, for turnaround- time is always an important criteria for IC fabs. But considering the design layout becomes more complicated and pattern density becomes higher with the shrinkage of the critical dimension, fragmentation control during the OPC procedure is also becoming more and more sophisticated. Thus, to achieve a convergent correction result for all OPC fragments within limited correction iteration times now becomes a big challenge to OPC engineers. This work presents our study in a new OPC iteration control methodology. It can help to find an algorithm that always converges, and reduce the excessive use of parameter setting, commands and other involvement by the user. With this, we can reduce the run time required to obtain a convergent OPC solution.

  1. Decentralized Control of Sound Radiation Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2009-01-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  2. Decentralized control of sound radiation using iterative loop recovery.

    PubMed

    Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

    2010-10-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

  3. Decentralized control of sound radiation using iterative loop recovery.

    PubMed

    Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

    2010-10-01

    A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units. PMID:20968346

  4. Multiagent reinforcement learning in the Iterated Prisoner's Dilemma.

    PubMed

    Sandholm, T W; Crites, R H

    1996-01-01

    Reinforcement learning (RL) is based on the idea that the tendency to produce an action should be strengthened (reinforced) if it produces favorable results, and weakened if it produces unfavorable results. Q-learning is a recent RL algorithm that does not need a model of its environment and can be used on-line. Therefore, it is well suited for use in repeated games against an unknown opponent. Most RL research has been confined to single-agent settings or to multiagent settings where the agents have totally positively correlated payoffs (team problems) or totally negatively correlated payoffs (zero-sum games). This paper is an empirical study of reinforcement learning in the Iterated Prisoner's Dilemma (IPD), where the agents' payoffs are neither totally positively nor totally negatively correlated. RL is considerably more difficult in such a domain. This paper investigates the ability of a variety of Q-learning agents to play the IPD game against an unknown opponent. In some experiments, the opponent is the fixed strategy Tit-For-Tat, while in others it is another Q-learner. All the Q-learners learned to play optimally against Tit-For-Tat. Playing against another learner was more difficult because the adaptation of the other learner created a non-stationary environment, and because the other learner was not endowed with any a priori knowledge about the IPD game such as a policy designed to encourage cooperation. The learners that were studied varied along three dimensions: the length of history they received as context, the type of memory they employed (lookup tables based on restricted history windows or recurrent neural networks that can theoretically store features from arbitrarily deep in the past), and the exploration schedule they followed. Although all the learners faced difficulties when playing against other learners, agents with longer history windows, lookup table memories, and longer exploration schedules fared best in the IPD games.

  5. Iterative LQG Controller Design Through Closed-Loop Identification

    NASA Technical Reports Server (NTRS)

    Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

    1996-01-01

    This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

  6. ITER Scenario Performance Simulations Assessing Control and Vertical Stability

    SciTech Connect

    Casper, T; Ferron, J; Humphreys, D; Jackson, G; Leuer, J; LoDestro, L; Luce, T; Meyer, W; Pearlstein, L; Welander, A

    2008-05-20

    In simulating reference scenarios proposed for ITER operation, we also explore performance of the poloidal field (PF) and central solenoid (CS) coil systems using a controller to maintain plasma shape and vertical stability during the discharge evolution. We employ a combination of techniques to evaluate system constraints and stability using time-dependent transport simulations of ITER discharges. We have begun the process of benchmarking these simulations with experiments on the DIII-D tokamak. Simulations include startup on the outside limiter, X-point formation and current ramp up to full power, plasma burn conditions at 15MA and 17MA, and ramp down at the end of the pulse. We also simulate perturbative events such as H-to-L back transitions. Our results indicate the viability of proposed ITER operating modes.

  7. Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

    PubMed

    Meadmore, Katie L; Cai, Zhonglun; Tong, Daisy; Hughes, Ann-Marie; Freeman, Chris T; Rogers, Eric; Burridge, Jane H

    2011-01-01

    A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation. PMID:22275698

  8. Sawtooth control in ITER using ion cyclotron resonance heating

    SciTech Connect

    Chapman, I. T.; Graves, J P; Johnson, T.; Asunta, O.; Bonoli, P.; Choi, M.; Jaeger, E. F.; Jucker, M.; Sauter, O.

    2011-01-01

    Numerical modeling of the effects of ion cyclotron resonance heating (ICRH) on the stability of the internal kink mode suggests that ICRH should be considered as an essential sawtooth control tool in ITER. Sawtooth control using ICRH is achieved by directly affecting the energy of the internal kink mode rather than through modification of the magnetic shear by driving localized currents. Consequently, ICRH can be seen as complementary to the planned electron cyclotron current drive actuator, and indeed will improve the efficacy of current drive schemes. Simulations of the ICRH distribution using independent RF codes give confidence in numerical predictions that the stabilizing influence of the fusion-born alphas can be negated by appropriately tailored minority (3)He ICRH heating in ITER. Finally, the effectiveness of all sawtooth actuators is shown to increase as the q = 1 surface moves towards the manetic axis, whilst the passive stabilization arising from the alpha and NBI particles decreases.

  9. Sawtooth control in ITER using ion cyclotron resonance heating

    NASA Astrophysics Data System (ADS)

    Chapman, I. T.; Graves, J. P.; Johnson, T.; Asunta, O.; Bonoli, P.; Choi, M.; Jaeger, E. F.; Jucker, M.; Sauter, O.

    2011-12-01

    Numerical modelling of the effects of ion cyclotron resonance heating (ICRH) on the stability of the internal kink mode suggests that ICRH should be considered as an essential sawtooth control tool in ITER. Sawtooth control using ICRH is achieved by directly affecting the energy of the internal kink mode rather than through modification of the magnetic shear by driving localized currents. Consequently, ICRH can be seen as complementary to the planned electron cyclotron current drive actuator, and indeed will improve the efficacy of current drive schemes. Simulations of the ICRH distribution using independent RF codes give confidence in numerical predictions that the stabilizing influence of the fusion-born alphas can be negated by appropriately tailored minority 3He ICRH heating in ITER. Finally, the effectiveness of all sawtooth actuators is shown to increase as the q = 1 surface moves towards the manetic axis, whilst the passive stabilization arising from the alpha and NBI particles decreases.

  10. Fixed Point Transformations Based Iterative Control of a Polymerization Reaction

    NASA Astrophysics Data System (ADS)

    Tar, József K.; Rudas, Imre J.

    As a paradigm of strongly coupled non-linear multi-variable dynamic systems the mathematical model of the free-radical polymerization of methyl-metachrylate with azobis (isobutyro-nitrile) as an initiator and toluene as a solvent taking place in a jacketed Continuous Stirred Tank Reactor (CSTR) is considered. In the adaptive control of this system only a single input variable is used as the control signal (the process input, i.e. dimensionless volumetric flow rate of the initiator), and a single output variable is observed (the process output, i.e. the number-average molecular weight of the polymer). Simulation examples illustrate that on the basis of a very rough and primitive model consisting of two scalar variables various fixed-point transformations based convergent iterations result in a novel, sophisticated adaptive control.

  11. Iterative exponential growth of stereo- and sequence-controlled polymers.

    PubMed

    Barnes, Jonathan C; Ehrlich, Deborah J C; Gao, Angela X; Leibfarth, Frank A; Jiang, Yivan; Zhou, Erica; Jamison, Timothy F; Johnson, Jeremiah A

    2015-10-01

    Chemists have long sought sequence-controlled synthetic polymers that mimic nature's biopolymers, but a practical synthetic route that enables absolute control over polymer sequence and structure remains a key challenge. Here, we report an iterative exponential growth plus side-chain functionalization (IEG+) strategy that begins with enantiopure epoxides and facilitates the efficient synthesis of a family of uniform >3 kDa macromolecules of varying sequence and stereoconfiguration that are coupled to produce unimolecular polymers (>6 kDa) with sequences and structures that cannot be obtained using traditional polymerization techniques. Selective side-chain deprotection of three hexadecamers is also demonstrated, which imbues each compound with the ability to dissolve in water. We anticipate that these new macromolecules and the general IEG+ strategy will find broad application as a versatile platform for the scalable synthesis of sequence-controlled polymers.

  12. Robust iterative learning protocols for finite-time consensus of multi-agent systems with interval uncertain topologies

    NASA Astrophysics Data System (ADS)

    Meng, Deyuan; Jia, Yingmin; Du, Junping

    2015-04-01

    This paper is devoted to the robust finite-time output consensus problems of multi-agent systems under directed graphs, where all agents and their communication topologies are subject to interval uncertainties. Distributed protocols are constructed by using iterative learning control (ILC) algorithms, where information is exchanged only at the end of one iteration and learning is used to update the control inputs after each iteration. It is proved that under ILC-based protocols, the finite-time consensus can be achieved with an increasing number of iterations if the communication network of agents is guaranteed to have a spanning tree. Moreover, if the information of any desired terminal output is available to a portion (not necessarily all) of the agents, then the consensus output that all agents finally reach can be enabled to be the desired terminal output. It is also proved that for all ILC-based protocols, gain selections can be provided in terms of bound values, and consensus conditions can be developed associated with bound matrices. Simulation results are given to demonstrate the effectiveness of our theoretical results.

  13. A strategy for sequence control in vinyl polymers via iterative controlled radical cyclization

    PubMed Central

    Hibi, Yusuke; Ouchi, Makoto; Sawamoto, Mitsuo

    2016-01-01

    There is a growing interest in sequence-controlled polymers toward advanced functional materials. However, control of side-chain order for vinyl polymers has been lacking feasibility in the field of polymer synthesis because of the inherent feature of chain-growth propagation. Here we show a general and versatile strategy to control sequence in vinyl polymers through iterative radical cyclization with orthogonally cleavable and renewable bonds. The proposed methodology employs a repetitive and iterative intramolecular cyclization via a radical intermediate in a one-time template with a radical-generating site at one end and an alkene end at the other, each of which is connected to a linker via independently cleavable and renewable bonds. The unique design specifically allowed control of radical addition reaction although inherent chain-growth intermediate (radical species) was used, as well as the iterative cycle and functionalization for resultant side chains, to lead to sequence-controlled vinyl polymers (or oligomers). PMID:26996881

  14. Linear decentralized learning control

    NASA Technical Reports Server (NTRS)

    Lee, Soo C.; Longman, Richard W.; Phan, Minh

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  15. Sawtooth control in JET with ITER relevant low field side resonance ion cyclotron resonance heating and ITER-like wall

    NASA Astrophysics Data System (ADS)

    Graves, J. P.; Lennholm, M.; Chapman, I. T.; Lerche, E.; Reich, M.; Alper, B.; Bobkov, V.; Dumont, R.; Faustin, J. M.; Jacquet, P.; Jaulmes, F.; Johnson, T.; Keeling, D. L.; Liu, Yueqiang; Nicolas, T.; Tholerus, S.; Blackman, T.; Carvalho, I. S.; Coelho, R.; Van Eester, D.; Felton, R.; Goniche, M.; Kiptily, V.; Monakhov, I.; Nave, M. F. F.; Perez von Thun, C.; Sabot, R.; Sozzi, C.; Tsalas, M.

    2015-01-01

    New experiments at JET with the ITER-like wall show for the first time that ITER-relevant low field side resonance first harmonic ion cyclotron resonance heating (ICRH) can be used to control sawteeth that have been initially lengthened by fast particles. In contrast to previous (Graves et al 2012 Nat. Commun. 3 624) high field side resonance sawtooth control experiments undertaken at JET, it is found that the sawteeth of L-mode plasmas can be controlled with less accurate alignment between the resonance layer and the sawtooth inversion radius. This advantage, as well as the discovery that sawteeth can be shortened with various antenna phasings, including dipole, indicates that ICRH is a particularly effective and versatile tool that can be used in future fusion machines for controlling sawteeth. Without sawtooth control, neoclassical tearing modes (NTMs) and locked modes were triggered at very low normalised beta. High power H-mode experiments show the extent to which ICRH can be tuned to control sawteeth and NTMs while simultaneously providing effective electron heating with improved flushing of high Z core impurities. Dedicated ICRH simulations using SELFO, SCENIC and EVE, including wide drift orbit effects, explain why sawtooth control is effective with various antenna phasings and show that the sawtooth control mechanism cannot be explained by enhancement of the magnetic shear. Hybrid kinetic-magnetohydrodynamic stability calculations using MISHKA and HAGIS unravel the optimal sawtooth control regimes in these ITER relevant plasma conditions.

  16. Magnetic Control of Locked Modes in Present Devices and ITER

    NASA Astrophysics Data System (ADS)

    Volpe, F. A.; Sabbagh, S.; Sweeney, R.; Hender, T.; Kirk, A.; La Haye, R. J.; Strait, E. J.; Ding, Y. H.; Rao, B.; Fietz, S.; Maraschek, M.; Frassinetti, L.; in, Y.; Jeon, Y.; Sakakihara, S.

    2014-10-01

    The toroidal phase of non-rotating (``locked'') neoclassical tearing modes was controlled in several devices by means of applied magnetic perturbations. Evidence is presented from various tokamaks (ASDEX Upgrade, DIII-D, JET, J-TEXT, KSTAR), spherical tori (MAST, NSTX) and a reversed field pinch (EXTRAP-T2R). Furthermore, the phase of interchange modes was controlled in the LHD helical device. These results share a common interpretation in terms of torques acting on the mode. Based on this interpretation, it is predicted that control-coil currents will be sufficient to control the phase of locking in ITER. This will be possible both with the internal coils and with the external error-field-correction coils, and might have promising consequences for disruption avoidance (by aiding the electron cyclotron current drive stabilization of locked modes), as well as for spatially distributing heat loads during disruptions. This work was supported in part by the US Department of Energy under DE-SC0008520, DE-FC-02-04ER54698 and DE-AC02-09CH11466.

  17. Indirect decentralized learning control

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Lee, Soo C.; Phan, M.

    1992-01-01

    The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

  18. The prefrontal cortex and hybrid learning during iterative competitive games.

    PubMed

    Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

    2011-12-01

    Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning.

  19. Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

    PubMed

    Wei, Qinglai; Liu, Derong; Lin, Hanquan

    2016-03-01

    In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

  20. Learning control system design based on 2-D theory - An application to parallel link manipulator

    NASA Technical Reports Server (NTRS)

    Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

    1990-01-01

    An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

  1. Not All Wizards Are from Oz: Iterative Design of Intelligent Learning Environments by Communication Capacity Tapering

    ERIC Educational Resources Information Center

    Mavrikis, Manolis; Gutierrez-Santos, Sergio

    2010-01-01

    This paper presents a methodology for the design of intelligent learning environments. We recognise that in the educational technology field, theory development and system-design should be integrated and rely on an iterative process that addresses: (a) the difficulty to elicit precise, concise, and operationalized knowledge from "experts" and (b)…

  2. Disruptions in ITER and strategies for their control and mitigation

    NASA Astrophysics Data System (ADS)

    Lehnen, M.; Aleynikova, K.; Aleynikov, P. B.; Campbell, D. J.; Drewelow, P.; Eidietis, N. W.; Gasparyan, Yu.; Granetz, R. S.; Gribov, Y.; Hartmann, N.; Hollmann, E. M.; Izzo, V. A.; Jachmich, S.; Kim, S.-H.; Kočan, M.; Koslowski, H. R.; Kovalenko, D.; Kruezi, U.; Loarte, A.; Maruyama, S.; Matthews, G. F.; Parks, P. B.; Pautasso, G.; Pitts, R. A.; Reux, C.; Riccardo, V.; Roccella, R.; Snipes, J. A.; Thornton, A. J.; de Vries, P. C.

    2015-08-01

    The thermal and electromagnetic loads related to disruptions in ITER are substantial and require careful design of tokamak components to ensure they reach the projected lifetime and to ensure that safety relevant components fulfil their function for the worst foreseen scenarios. The disruption load specifications are the basis for the design process of components like the full-W divertor, the blanket modules and the vacuum vessel and will set the boundary conditions for ITER operations. This paper will give a brief overview on the disruption loads and mitigation strategies for ITER and will discuss the physics basis which is continuously refined through the current disruption R&D programs.

  3. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  4. Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

    PubMed

    Dutta, Samrat; Patchaikani, Prem Kumar; Behera, Laxmidhar

    2016-07-01

    This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system. PMID:26259150

  5. Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

    PubMed

    Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

    2013-10-01

    This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example. PMID:24808590

  6. Analysis of Distribution of Time Scores in Iterative Learning Type Courseware Using Fourier Transform

    NASA Astrophysics Data System (ADS)

    Watanabe, Hiroyuki

    In this research, an iterative learning type courseware was made, the distribution of time scores in the courseware is gotten by the learning management system. It is a proposed method by which the distribution of time scores is changed to frequency and to power spectrum using Fourier Transform. The learning process continues until students get the passing scores and are classified by using these values, which are related to average time and the average of scores‧ square. Furthermore, the cross-correlation coefficients between the standard student and students are calculated, and delay times are analyzed. Finally, the transfer functions of some students are calculated, and the characteristics of the learning processes are analyzed.

  7. A policy iteration approach to online optimal control of continuous-time constrained-input systems.

    PubMed

    Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

    2013-09-01

    This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. PMID:23706414

  8. A theoretical analysis of temporal difference learning in the iterated prisoner's dilemma game.

    PubMed

    Masuda, Naoki; Ohtsuki, Hisashi

    2009-11-01

    Direct reciprocity is a chief mechanism of mutual cooperation in social dilemma. Agents cooperate if future interactions with the same opponents are highly likely. Direct reciprocity has been explored mostly by evolutionary game theory based on natural selection. Our daily experience tells, however, that real social agents including humans learn to cooperate based on experience. In this paper, we analyze a reinforcement learning model called temporal difference learning and study its performance in the iterated Prisoner's Dilemma game. Temporal difference learning is unique among a variety of learning models in that it inherently aims at increasing future payoffs, not immediate ones. It also has a neural basis. We analytically and numerically show that learners with only two internal states properly learn to cooperate with retaliatory players and to defect against unconditional cooperators and defectors. Four-state learners are more capable of achieving a high payoff against various opponents. Moreover, we numerically show that four-state learners can learn to establish mutual cooperation for sufficiently small learning rates.

  9. Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    NASA Astrophysics Data System (ADS)

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-12-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

  10. Nuclear Safety Functions of ITER Gas Injection System Instrumentation and Control and the Concept Design

    NASA Astrophysics Data System (ADS)

    Yang, Yu; Maruyama, S.; Fossen, A.; Villers, F.; Kiss, G.; Zhang, Bo; Li, Bo; Jiang, Tao; Huang, Xiangmei

    2016-08-01

    The ITER Gas Injection System (GIS) plays an important role on fueling, wall conditioning and distribution for plasma operation. Besides that, to support the safety function of ITER, GIS needs to implement three nuclear safety Instrumentation and Control (I&C) functions. In this paper, these three functions are introduced with the emphasis on their latest safety classifications. The nuclear I&C design concept is briefly discussed at the end.

  11. Iterative development of visual control systems in a research vivarium.

    PubMed

    Bassuk, James A; Washington, Ida M

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children's (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart's Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected Principles

  12. Iterative Development of Visual Control Systems in a Research Vivarium

    PubMed Central

    Bassuk, James A.; Washington, Ida M.

    2014-01-01

    The goal of this study was to test the hypothesis that reintroduction of Continuous Performance Improvement (CPI) methodology, a lean approach to management at Seattle Children’s (Hospital, Research Institute, Foundation), would facilitate engagement of vivarium employees in the development and sustainment of a daily management system and a work-in-process board. Such engagement was implemented through reintroduction of aspects of the Toyota Production System. Iterations of a Work-In-Process Board were generated using Shewhart’s Plan-Do-Check-Act process improvement cycle. Specific attention was given to the importance of detecting and preventing errors through assessment of the following 5 levels of quality: Level 1, customer inspects; Level 2, company inspects; Level 3, work unit inspects; Level 4, self-inspection; Level 5, mistake proofing. A functioning iteration of a Mouse Cage Work-In-Process Board was eventually established using electronic data entry, an improvement that increased the quality level from 1 to 3 while reducing wasteful steps, handoffs and queues. A visual workplace was realized via a daily management system that included a Work-In-Process Board, a problem solving board and two Heijunka boards. One Heijunka board tracked cage changing as a function of a biological kanban, which was validated via ammonia levels. A 17% reduction in cage changing frequency provided vivarium staff with additional time to support Institute researchers in their mutual goal of advancing cures for pediatric diseases. Cage washing metrics demonstrated an improvement in the flow continuum in which a traditional batch and queue push system was replaced with a supermarket-type pull system. Staff engagement during the improvement process was challenging and is discussed. The collective data indicate that the hypothesis was found to be true. The reintroduction of CPI into daily work in the vivarium is consistent with the 4P Model of the Toyota Way and selected

  13. Robust model predictive control by iterative optimisation for polytopic uncertain systems

    NASA Astrophysics Data System (ADS)

    Wang, Chuanxu

    2012-09-01

    This article addresses robust model predictive control (MPC) for constrained systems with polytopic uncertainty description. Firstly, in the technique which parametrises the infinite horizon control moves into a single state feedback law and invokes the parameter-dependent Lyapunov method for achieving closed-loop stability, the slack matrices are iteratively solved at each sampling time. Secondly, in the technique which parametrises the infinite horizon control moves into a set of free perturbations followed by a single state feedback law, the feedback gains within the switch horizon are iteratively found at each sampling time, rather than just inherited from the previous sampling time. Numerical examples show that iterative MPC can not only improve the control performance, but also enlarge the region of attraction.

  14. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution.

    PubMed

    Wangmeng Zuo; Dongwei Ren; Zhang, David; Shuhang Gu; Lei Zhang

    2016-04-01

    Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method. PMID:26915121

  15. e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

    NASA Astrophysics Data System (ADS)

    Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

    2016-02-01

    XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

  16. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  17. Multiagent reinforcement learning: spiking and nonspiking agents in the iterated Prisoner's Dilemma.

    PubMed

    Vassiliades, Vassilis; Cleanthous, Aristodemos; Christodoulou, Chris

    2011-04-01

    This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and nonspiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. The spiking agents are neural networks with leaky integrate-and-fire neurons trained with two different learning algorithms: 1) reinforcement of stochastic synaptic transmission, or 2) reward-modulated spike-timing-dependent plasticity with eligibility trace. The nonspiking agents use a tabular representation and are trained with Q- and SARSA learning algorithms, with a novel reward transformation process also being applied to the Q-learning agents. According to the results, the cooperative outcome is enhanced by: 1) transformed internal reinforcement signals and a combination of a high learning rate and a low discount factor with an appropriate exploration schedule in the case of non-spiking agents, and 2) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and nonspiking agents have similar behavior and therefore they can equally well be used in a multiagent interaction setting. For training the spiking agents in the case where more than one output neuron competes for reinforcement, a novel and necessary modification that enhances competition is applied to the two learning algorithms utilized, in order to avoid a possible synaptic saturation. This is done by administering to the networks additional global reinforcement signals for every spike of the output neurons that were not "responsible" for the preceding decision. PMID:21421435

  18. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  19. Co-Simulation Research of the Mechanical-Hydraulic-Control Coupling System of ITER Tractor

    NASA Astrophysics Data System (ADS)

    Yang, Xiuqing; Luo, Minzhou; Mei, Tao; Yao, Damao

    2009-06-01

    The virtual prototyping models of the mechanical, hydraulic and control system of the ITER tractor were built with CATIA, ADAMS and MATLAB/Simulink respectively according to its heavy load and high precision characteristics, and the data transfer between the different models was accomplished by the integration interface between different software. Consequently the virtual experimental platform for the multi-disciplinary co-simulation was established. A co-simulation study of the mechanical-hydraulic-control coupling system of the ITER tractor was carried out. The synchronization servo control of parallel hydraulic cylinders was implemented, and the tracking control of the preconcerted trajectory of the hydraulic cylinders was realized on the established experimental platform. This paper presents the optimization design and technology rebuilding for the complicated coupling system with its theoretic foundation and co-simulation virtual experimental platform.

  20. Toward a design for the ITER plasma shape and stability control system

    SciTech Connect

    Humphreys, D.A.; Leuer, J.A.; Kellman, A.G.; Haney, S.W.; Bulmer, R.H.; Pearlstein, L.D.; Portone, A.

    1994-07-01

    A design strategy for an integrated shaping and stability control algorithm for ITER is described. This strategy exploits the natural multivariable nature of the system so that all poloidal field coils are used to simultaneously control all regulated plasma shape and position parameters. A nonrigid, flux-conserving linearized plasma response model is derived using a variational procedure analogous to the ideal MHD Extended Energy Principle. Initial results are presented for the non-rigid plasma response model approach applied to an example DIII-D equilibrium. For this example, the nonrigid model is found to yield a higher passive growth rate than a rigid current-conserving plasma response model. Multivariable robust controller design methods are discussed and shown to be appropriate for the ITER shape control problem.

  1. Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

    ERIC Educational Resources Information Center

    Apter, Brian

    2014-01-01

    An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

  2. How to Combine Objectives and Methods of Evaluation in Iterative ILE Design: Lessons Learned from Designing Ambre-Add

    ERIC Educational Resources Information Center

    Nogry, S.; Jean-Daubias, S.; Guin, N.

    2012-01-01

    This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…

  3. An iterative investigation into the implementation of handheld computers as learning tools in a science museum

    NASA Astrophysics Data System (ADS)

    Phipps, Molly E.

    In this study I discuss the state of Free-Choice Learning research, and an investigation into the use of personal ubiquitous technology on visitors' experiences at a science center. The three manuscripts included in this document: (1) Review published research on free-choice learning from 1997-2007 from selected journals (2) Examine visitors' interest in using handheld computers (iPods) for learning in a science museum, and report on refining protocols for this type of research. (3) Investigate the impact of using an iPod with supplementary videos on visitors use and understanding of an exhibit on scientific chaos. This study was approached in two phases, the first phase follows the principles of design research in exploring ways to present the iPods within the most favorable context to encourage learning. These changes were systematically implemented and their impact on visitors' experiences were documented. The second phase of the research focused on one particular exhibit and three accompanying videos on the iPod. This exhibit is well loved, but difficult to understand for visitors and docents alike. Through naturalistic inquiry and iterative open coding, I found visitors interpreted appropriate use of the exhibit in four distinct ways: HOW DOES IT WORK?, WAITING FOR THE SPLASH, INTERACTING, and RESTING. However, iPod users all interpreted appropriate use of the exhibit as HOW DOES IT WORK?. Careful observation of visitors' actions at the "Chaos Wheel" exhibit suggests that the exhibit needs some revision if it is to become more accessible to more visitors. The iPod represents one way to increase the accessibility of the exhibit, but other means should be explored.

  4. Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification.

    PubMed

    Alsahwa, Bassem; Solaiman, Basel; Almouahed, Shaban; Bosse, Eloi; Gueriot, Didier

    2016-08-01

    This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity. PMID:27305673

  5. An iterative approach to the optimal co-design of linear control systems

    NASA Astrophysics Data System (ADS)

    Jiang, Yu; Wang, Yebin; Bortoff, Scott A.; Jiang, Zhong-Ping

    2016-04-01

    This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.

  6. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.

  7. Iterated non-linear model predictive control based on tubes and contractive constraints.

    PubMed

    Murillo, M; Sánchez, G; Giovanini, L

    2016-05-01

    This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. PMID:26850752

  8. Procedural Learning during Declarative Control

    ERIC Educational Resources Information Center

    Crossley, Matthew J.; Ashby, F. Gregory

    2015-01-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

  9. Decentralized Control of Sound Radiation from an Aircraft-Style Panel Using Iterative Loop Recovery

    NASA Technical Reports Server (NTRS)

    Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

    2008-01-01

    A decentralized LQG-based control strategy is designed to reduce low-frequency sound transmission through periodically stiffened panels. While modern control strategies have been used to reduce sound radiation from relatively simple structural acoustic systems, significant implementation issues have to be addressed before these control strategies can be extended to large systems such as the fuselage of an aircraft. For instance, centralized approaches typically require a high level of connectivity and are computationally intensive, while decentralized strategies face stability problems caused by the unmodeled interaction between neighboring control units. Since accurate uncertainty bounds are not known a priori, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is validated using real-time control experiments performed on a built-up aluminum test structure representative of the fuselage of an aircraft. Experiments demonstrate that the iterative approach is capable of achieving 12 dB peak reductions and a 3.6 dB integrated reduction in radiated sound power from the stiffened panel.

  10. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  11. Towards Current Profile Control in ITER: Potential Approaches and Research Needs

    NASA Astrophysics Data System (ADS)

    Schuster, E.; Barton, J. E.; Wehner, W. P.

    2014-10-01

    Many challenging plasma control problems still need to be addressed in order for the ITER Plasma Control System (PCS) to be able to successfully achieve the ITER project goals. For instance, setting up a suitable toroidal current density profile is key for one possible advanced scenario characterized by noninductive sustainment of the plasma current and steady-state operation. The nonlinearity and high dimensionality exhibited by the plasma demand a model-based current-profile control synthesis procedure that can accommodate this complexity through embedding the known physics within the design. The development of a model capturing the dynamics of the plasma relevant for control design enables not only the design of feedback controllers for regulation or tracking but also the design of optimal feedforward controllers for a systematic model-based approach to scenario planning, the design of state estimators for a reliable real-time reconstruction of the plasma internal profiles based on limited and noisy diagnostics, and the development of a fast predictive simulation code for closed-loop performance evaluation before implementation. Progress towards control-oriented modeling of the current profile evolution and associated control design has been reported following both data-driven and first-principles-driven approaches. An overview of these two approaches will be provided, as well as a discussion on research needs associated with each one of the model applications described above. Supported by the US Department of Energy under DE-SC0001334 and DE-SC0010661.

  12. Current Control in ITER Steady State Plasmas With Neutral Beam Steering

    SciTech Connect

    R.V. Budny

    2009-09-10

    Predictions of quasi steady state DT plasmas in ITER are generated using the PTRANSP code. The plasma temperatures, densities, boundary shape, and total current (9 - 10 MA) anticipated for ITER steady state plasmas are specified. Current drive by negative ion neutral beam injection, lower-hybrid, and electron cyclotron resonance are calculated. Four modes of operation with different combinations of current drive are studied. For each mode, scans with the NNBI aimed at differing heights in the plasma are performed to study effects of current control on the q profile. The timeevolution of the currents and q are calculated to evaluate long duration transients. Quasi steady state, strongly reversed q profiles are predicted for some beam injection angles if the current drive and bootstrap currents are sufficiently large.

  13. Learning control of robotic manipulators

    NASA Astrophysics Data System (ADS)

    Tai, Heng-Ming; Chen, Yu-Che

    1992-09-01

    In this paper, we propose a learning control scheme for direct trajectory control of robotic manipulators. The main features are that we use a priori structure knowledge of robot dynamics in the design and the neural networks are not used to learn inverse dynamic models. The neural network controller is utilized to compensate the deviation due to the approximate models of robotic manipulators. In addition, true teaching signals of the neural network compensators are employed in the learning phase. Simulations are conducted to show the feasibility of the proposed method.

  14. Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation.

    PubMed

    Koyuncu, Can Fahrettin; Akhan, Ece; Ersahin, Tulin; Cetin-Atalay, Rengul; Gunduz-Demir, Cigdem

    2016-04-01

    Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry.

  15. Spine detection in CT and MR using iterated marginal space learning.

    PubMed

    Michael Kelm, B; Wels, Michael; Kevin Zhou, S; Seifert, Sascha; Suehling, Michael; Zheng, Yefeng; Comaniciu, Dorin

    2013-12-01

    Examinations of the spinal column with both, Magnetic Resonance (MR) imaging and Computed Tomography (CT), often require a precise three-dimensional positioning, angulation and labeling of the spinal disks and the vertebrae. A fully automatic and robust approach is a prerequisite for an automated scan alignment as well as for the segmentation and analysis of spinal disks and vertebral bodies in Computer Aided Diagnosis (CAD) applications. In this article, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the spinal disks. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Finally, we propose an optional case-adaptive segmentation approach that allows to segment the spinal disks and vertebrae in MR and CT respectively. Since the proposed approaches are learning-based, they can be trained for MR or CT alike. Experimental results based on 42 MR and 30 CT volumes show that our system not only achieves superior accuracy but also is among the fastest systems of its kind in the literature. On the MR data set the spinal disks of a whole spine are detected in 11.5s on average with 98.6% sensitivity and 0.073 false positive detections per volume. On the CT data a comparable sensitivity of 98.0% with 0.267 false positives is achieved. Detected disks are localized with an average position error of 2.4 mm/3.2 mm and angular error of 3.9°/4.5° in MR/CT, which is close to the employed hypothesis resolution of 2.1 mm and 3.3°. PMID:23265800

  16. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    The drive for the journey to Mars is in a higher gear than ever before. We are developing new spacecraft and life support systems to take humans to the Red Planet. The journey that development hardware takes before its final incarnation in a fully integrated spacecraft can take years, as is the case for the Orion environmental control and life support system (ECLSS). Through the Pressure Integrated Suit Test (PIST) series, NASA personnel at Johnson Space Center have been characterizing the behavior of a closed loop ECLSS in the event of cabin depressurization. This kind of testing - one of the most hazardous activities performed at JSC - requires an iterative approach, increasing in complexity and hazards). The PIST series, conducted in the Crew and Thermal Systems Division (CTSD) 11-ft Chamber, started with unmanned test precursors before moving to a human-in-the-loop phase, and continues to evolve with the eventual goal of a qualification test for the final system that will be installed on Orion. Meanwhile, the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) program is an effort to research and develop technologies that will work in concert to support habitation on Mars. September 2015 marked the first unmanned HESTIA test, with the goal of characterizing how ECLSS technologies work together in a closed environment. HESTIA will culminate in crewed testing, but it can benefit from the lessons learned from another test that is farther ahead in its development and life cycle. Discussing PIST and HESTIA, this paper illustrates how we approach testing, the kind of information that facility teams need to ensure efficient collaborations and successful testing, and how we can apply what we learn to execute future tests.

  17. Aircraft adaptive learning control

    NASA Technical Reports Server (NTRS)

    Lee, P. S. T.; Vanlandingham, H. F.

    1979-01-01

    The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

  18. eNOSHA, a Free, Open and Flexible Learning Object Repository--An Iterative Development Process for Global User-Friendliness

    ERIC Educational Resources Information Center

    Mozelius, Peter; Hettiarachchi, Enosha

    2012-01-01

    This paper describes the iterative development process of a Learning Object Repository (LOR), named eNOSHA. Discussions on a project for a LOR started at the e-Learning Centre (eLC) at The University of Colombo, School of Computing (UCSC) in 2007. The eLC has during the last decade been developing learning content for a nationwide e-learning…

  19. Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

    NASA Astrophysics Data System (ADS)

    Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

    2016-05-01

    In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

  20. An Application of Fictitious Reference Iterative Tuning to State Feedback Control

    NASA Astrophysics Data System (ADS)

    Matsui, Yoshihiro; Akamatsu, Shunichi; Kimura, Tomohiko; Nakano, Kazushi; Sakurama, Kazunori

    In this paper, an application method of Fictitious Reference Iterative Tuning (FRIT), which has been developed for controller gain tuning for single-input single-output systems, to state feedback gain tuning for single-input multivariable systems is proposed. Transient response data of a single-input multivariable plant obtained under closed-loop operation is used for model matching by the FRIT in time domain. The data is also used in frequency domain to estimate the stability and to improve the control performance of the closed-loop system with the state feedback gain tuned by the method. The method is applied to a state feedback control system for an inverted pendulum with an inertia rotor and its usefulness is illustrated through experiments.

  1. Numerical analysis of a reinforcement learning model with the dynamic aspiration level in the iterated Prisoner's dilemma.

    PubMed

    Masuda, Naoki; Nakamura, Mitsuhiro

    2011-06-01

    Humans and other animals can adapt their social behavior in response to environmental cues including the feedback obtained through experience. Nevertheless, the effects of the experience-based learning of players in evolution and maintenance of cooperation in social dilemma games remain relatively unclear. Some previous literature showed that mutual cooperation of learning players is difficult or requires a sophisticated learning model. In the context of the iterated Prisoner's dilemma, we numerically examine the performance of a reinforcement learning model. Our model modifies those of Karandikar et al. (1998), Posch et al. (1999), and Macy and Flache (2002) in which players satisfy if the obtained payoff is larger than a dynamic threshold. We show that players obeying the modified learning mutually cooperate with high probability if the dynamics of threshold is not too fast and the association between the reinforcement signal and the action in the next round is sufficiently strong. The learning players also perform efficiently against the reactive strategy. In evolutionary dynamics, they can invade a population of players adopting simpler but competitive strategies. Our version of the reinforcement learning model does not complicate the previous model and is sufficiently simple yet flexible. It may serve to explore the relationships between learning and evolution in social dilemma situations.

  2. 3D vacuum magnetic field modelling of the ITER ELM control coil during standard operating scenarios

    NASA Astrophysics Data System (ADS)

    Evans, T. E.; Orlov, D. M.; Wingen, A.; Wu, W.; Loarte, A.; Casper, T. A.; Schmitz, O.; Saibene, G.; Schaffer, M. J.; Daly, E.

    2013-09-01

    In-vessel, non-axisymmetric, control coils have proven to be an important option for mitigating and suppressing edge-localized modes (ELMs) in high performance operating regimes on a growing number of tokamaks. Additionally, an in-vessel non-axisymmetric ELM control coil is being considered in the ITER baseline design. In preparing for the initial operation of this coil set, a comprehensive study was carried out to characterize the linear superposition of the 3D vacuum magnetic field, produced by the ELM coil, on a series of equilibria representing nine standard ITER operating scenarios. Here, the spatial phase angle of toroidally distributed currents, specified with a cosine waveform, in the upper and lower rows of the ITER ELM coil (IEC) set is varied in 2° steps while holding the current in the equatorial row of coils constant. The peak current in each of the three toroidal rows of window-frame coils making up the IEC is scanned between 5 kAt and 90 kAt in 5 kAt steps and the width of the edge region covered by overlapping vacuum field magnetic islands is calculated. This width is compared to a vacuum field ELM suppression correlation criterion found in DIII-D. A minimum coil current satisfying the DIII-D criterion, along with an associated set of phase angles, is identified for each ITER operating scenario. These currents range from 20 kAt to 75 kAt depending on the operating scenario being used and the toroidal mode number (n) of the cosine waveform. Comparisons between the scaling of the divertor footprint area in cases with n = 3 perturbation fields versus those with n = 4 show significant advantages when using n = 3. In addition, it is found that the DIII-D correlation criterion can be satisfied in the event that various combinations of individual IEC window-frame coils need to be turned off due to malfunctioning components located inside the vacuum vessel. Details of these results for both the full set of 27 window-frame coils and various reduced sets

  3. Analysis of Iterative Waterfilling Algorithm for Multiuser Power Control in Digital Subscriber Lines

    NASA Astrophysics Data System (ADS)

    Luo, Zhi-Quan; Pang, Jong-Shi

    2006-12-01

    We present an equivalent linear complementarity problem (LCP) formulation of the noncooperative Nash game resulting from the DSL power control problem. Based on this LCP reformulation, we establish the linear convergence of the popular distributed iterative waterfilling algorithm (IWFA) for arbitrary symmetric interference environment and for certain asymmetric channel conditions with any number of users. In the case of symmetric interference crosstalk coefficients, we show that the users of IWFA in fact, unknowingly but willingly, cooperate to minimize a common quadratic cost function whose gradient measures the received signal power from all users. This is surprising since the DSL users in the IWFA have no intention to cooperate as each maximizes its own rate to reach a Nash equilibrium. In the case of asymmetric coefficients, the convergence of the IWFA is due to a contraction property of the iterates. In addition, the LCP reformulation enables us to solve the DSL power control problem under no restrictions on the interference coefficients using existing LCP algorithms, for example, Lemke's method. Indeed, we use the latter method to benchmark the empirical performance of IWFA in the presence of strong crosstalk interference.

  4. Real-time sawtooth control and neoclassical tearing mode preemption in ITER

    SciTech Connect

    Kim, D. Goodman, T. P.; Sauter, O.

    2014-06-15

    Real-time control of multiple plasma actuators is a requirement in advanced tokamaks; for example, for burn control, plasma current profile control and MHD stabilization—electron cyclotron (EC) wave absorption is ideally suited especially for the latter. On ITER, 24 EC sources can be switched between 56 inputs at the torus. In the torus, 5 launchers direct the power to various locations across the plasma profile via 11 steerable mirrors. For optimal usage of the available power, the aiming and polarization of the beams must be adapted to the plasma configuration and the needs of the scenario. Since the EC system performs many competing tasks, present day systems should demonstrate the ability of an EC plant to deal with several targets in parallel and/or to switch smoothly between goals to attain overall satisfaction. Based on pacing and locking experiments performed on TCV (Tokamak à Configuration Variable), the real-time sawtooth control of ITER with this complex set of actuators is analyzed, as an example. It is shown that sawtooth locking and pacing are possible with various levels of powers, leading to different time delays between the end of the EC power phase and the next sawtooth crash. This timing is important since it allows use of the same launchers for neoclassical tearing mode (NTM) preemption at the q = 1.5 or 2 surface, avoiding the need to switch power between launchers. These options are presented. It is also demonstrated that increasing the total EC power does not necessarily increase the range of control because of the geometry of the launchers.

  5. Tuberculosis control learning games.

    PubMed

    Smith, I

    1993-07-01

    In teaching health workers about tuberculosis (TB) control we frequently concentrate on the technological aspects, such as diagnosis, treatment and recording. Health workers also need to understand the sociological aspects of TB control, particularly those that influence the likelihood of diagnosis and cure. Two games are presented that help health workers comprehend the reasons why TB patients often delay in presenting for diagnosis, and why they then frequently default from treatment. PMID:8356734

  6. 17th Workshop on MHD Stability Control: addressing the disruption challenge for ITER

    NASA Astrophysics Data System (ADS)

    Buttery, Richard

    2013-08-01

    This annual workshop on magnetohydrodynamic stability control was held on 5-7 November 2012 at Columbia University in the city of New York, in the aftermath of a violent hydrodynamic instability event termed 'Hurricane Sandy'. Despite these challenging circumstances, Columbia University managed an excellent meeting, enabling the full participation of the community. This Workshop has been held since 1996 to help in the development of understanding and control of magnetohydrodynamic (MHD) instabilities for future fusion reactors. It covers a wide range of stability topics—from disruptions, to tearing modes, error fields, edge-localized modes (ELMs), resistive wall modes (RWMs) and ideal MHD—spanning many device types (tokamaks, stellarators and reversed field pinches) to identify commonalities in the physics and a means of control. The theme for 2012 was 'addressing the disruption challenge for ITER', and thus the first day had a heavy focus on both the avoidance and mitigation of disruptions in ITER. Key elements included understanding how to apply 3D fields to maintain stability, as well as managing the disruption process itself through mitigating loads in the thermal quench and handling so called 'runaway electrons'. This culminated in a panel discussion on the disruption mitigation strategy for ITER, which noted that heat load asymmetries during the thermal quench appear to be an artifact of MHD processes, and that runaway electron generation may be inevitable, suggesting research should focus on control and dissipation of the runaway beam. The workshop was combined this year with the annual US-Japan MHD Workshop, with a special section looking more deeply at 'Fundamentals of 3D Perturbed Equilibrium Control', with interesting sessions on 3D equilibrium reconstruction, RWM physics, novel control concepts such as non-magnetic sensing, adaptive control, q < 2 tokamak operation, and the effects of flow. The final day turned to tearing mode interactions

  7. Iterative algorithm analysis for phase-only diffractive control access system

    NASA Astrophysics Data System (ADS)

    Mihailescu, Mona; Preda, Alexandru; Cojoc, Dan; Scarlat, Eugen; Preda, Liliana

    2007-08-01

    A new architecture with two phases-only diffractive elements and one decryption mask for optical control access system is presented. Only three different persons which keep this element have the permission to access together. The Iterative Fourier Transform Algorithm (IFTA) is analyzed for phase-only diffractive optical elements (PODE) design with different constraints in the input and output plane and the optimal variant is chosen for better image quality in the output plane (big value for diffraction efficiency and small value for merit function and signal to noise ratio). For higher security we propose different incident waves. That are compared with the case when the first phase-only diffractive element and decryption masks are designed together in an extended iteration and the output images of them (first desired image) is taken over the second phase-only diffractive element. In order to increase security level, this finally PODE are designed to increase some parts from the first desired image. Only with this condition the key image on the detector is formed.

  8. Examination of the Entry to Burn and Burn Control for the ITER 15 MA Baseline and Other Scenarios

    SciTech Connect

    Kesse, Charles E.; Kim, S-H.; Koechl, F.

    2014-09-01

    The entry to burn and flattop burn control in ITER will be a critical need from the first DT experiments. Simulations are used to address time-dependent behavior under a range of possible conditions that include injected power level, impurity content (W, Ar, Be), density evolution, H-mode regimes, controlled parameter (Wth, Pnet, Pfusion), and actuator (Paux, fueling, fAr), with a range of transport models. A number of physics issues at the L-H transition require better understanding to project to ITER, however, simulations indicate viable control with sufficient auxiliary power (up to 73 MW), while lower powers become marginal (as low as 43 MW).

  9. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

    PubMed

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

  10. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  11. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

    PubMed

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  12. Metacognitive Control and Optimal Learning

    ERIC Educational Resources Information Center

    Son, Lisa K.; Sethi, Rajiv

    2006-01-01

    The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake…

  13. Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

    ERIC Educational Resources Information Center

    Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

    2015-01-01

    Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

  14. On the Sequential Control of ITER Poloidal Field Converters for Reactive Power Reduction

    NASA Astrophysics Data System (ADS)

    Yuan, Hongwen; Fu, Peng; Gao, Ge; Huang, Liansheng; Song, Zhiquan; He, Shiying; Wu, Yanan; Dong, Lin; Wang, Min; Fang, Tongzhen

    2014-12-01

    Sequential control applied to the International Thermonuclear Experimental Reactor (ITER) poloidal field converter system for the purpose of reactive power reduction is the subject of this investigation. Due to the inherent characteristics of thyristor-based phase-controlled converter, the poloidal field converter system consumes a huge amount of reactive power from the grid, which subsequently results in a voltage drop at the 66 kV busbar if no measure is taken. The installation of a static var compensator rated for 750 MVar at the 66 kV busbar is an essential way to compensate reactive power to the grid, which is the most effective measure to solve the problem. However, sequential control of the multi-series converters provides an additional method to improve the natural power factor and thus alleviate the pressure of reactive power demand of the converter system without any additional cost. In the present paper, by comparing with the symmetrical control technique, the advantage of sequential control in reactive power consumption is highlighted. Simulation results based on SIMULINK are found in agreement with the theoretical analysis.

  15. Performance improvement of robots using a learning control scheme

    NASA Technical Reports Server (NTRS)

    Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

    1987-01-01

    Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

  16. SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection

    SciTech Connect

    Shah, Anuj R.; Oehmen, Chris S.; Webb-Robertson, Bobbie-Jo M.

    2008-03-15

    Motivation: As the amount of biological sequence data continues to grow exponentially we face the increasing challenge of assigning function to this enormous molecular ‘parts list’. The most popular approaches to this challenge make use of the simplifying assumption that similar functional molecules, or proteins, sometimes have similar composition, or sequence. However, these algorithms often fail to identify remote homologs (proteins with similar function but dissimilar sequence) which often are a significant fraction of the total homolog collection for a given sequence. We introduce a Support Vector Machine (SVM)-based tool to detect Homology Using Semisupervised iTerative LEarning (SVM-HUSTLE) that detects significantly more remote homologs than current state-of-the-art sequence or cluster-based methods. As opposed to building profiles or position specific scoring matrices, SVM-HUSTLE builds an SVM classifier for a query sequence by training on a collection of representative highconfidence training sets. SVM-HUSTLE combines principles of semi-supervised learning theory with statistical sampling to create many concurrent classifiers to iteratively detect and refine on-the-fly patterns indicating homology. Results: When compared against existing methods for identifying protein homologs (BLASTp, PSI-BLAST, RANKPROP, MOTIFPROP and their variants) on the SCOP 1.59 benchmark dataset consisting of 7329 protein sequences, SVM-HUSTLE significantly outperforms each of the above methods using the most stringent ROC1 statistic with p-values less than 1e-20.

  17. Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

    PubMed

    Guo, Wentao; Liu, Feng; Si, Jennie; He, Dawei; Harley, Ronald; Mei, Shengwei

    2016-08-01

    The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

  18. Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation

    NASA Astrophysics Data System (ADS)

    Loarte, A.; Huijsmans, G.; Futatani, S.; Baylor, L. R.; Evans, T. E.; Orlov, D. M.; Schmitz, O.; Becoulet, M.; Cahyna, P.; Gribov, Y.; Kavin, A.; Sashala Naik, A.; Campbell, D. J.; Casper, T.; Daly, E.; Frerichs, H.; Kischner, A.; Laengner, R.; Lisgo, S.; Pitts, R. A.; Saibene, G.; Wingen, A.

    2014-03-01

    Progress in the definition of the requirements for edge localized mode (ELM) control and the application of ELM control methods both for high fusion performance DT operation and non-active low-current operation in ITER is described. Evaluation of the power fluxes for low plasma current H-modes in ITER shows that uncontrolled ELMs will not lead to damage to the tungsten (W) divertor target, unlike for high-current H-modes in which divertor damage by uncontrolled ELMs is expected. Despite the lack of divertor damage at lower currents, ELM control is found to be required in ITER under these conditions to prevent an excessive contamination of the plasma by W, which could eventually lead to an increased disruptivity. Modelling with the non-linear MHD code JOREK of the physics processes determining the flow of energy from the confined plasma onto the plasma-facing components during ELMs at the ITER scale shows that the relative contribution of conductive and convective losses is intrinsically linked to the magnitude of the ELM energy loss. Modelling of the triggering of ELMs by pellet injection for DIII-D and ITER has identified the minimum pellet size required to trigger ELMs and, from this, the required fuel throughput for the application of this technique to ITER is evaluated and shown to be compatible with the installed fuelling and tritium re-processing capabilities in ITER. The evaluation of the capabilities of the ELM control coil system in ITER for ELM suppression is carried out (in the vacuum approximation) and found to have a factor of ˜2 margin in terms of coil current to achieve its design criterion, although such a margin could be substantially reduced when plasma shielding effects are taken into account. The consequences for the spatial distribution of the power fluxes at the divertor of ELM control by three-dimensional (3D) fields are evaluated and found to lead to substantial toroidal asymmetries in zones of the divertor target away from the separatrix

  19. Stimulus control and associative learning.

    PubMed Central

    Williams, B A

    1984-01-01

    Interest in operant research on stimulus control has declined at the same time that much interest has burgeoned in nonoperant areas. Several examples of this shift toward traditional learning theory are considered, all of which have sponsored theoretical approaches that attempt to characterize the underlying associative units. These theoretical approaches are defended on the grounds that they have generated a deeper understanding of a variety of often puzzling phenomena. My projection is that future research will be determined even more strongly by theories about the structure of associations. Particular issues for which such discussion will have major impact include (1) whether conditional stimulus control is qualitatively different than simpler forms of stimulus control, (2) whether stimulus control is organized hierarchically, and (3) the origin of categories of stimulus equivalence. PMID:6520579

  20. Fast and automatic depth control of iterative bone ablation based on optical coherence tomography data

    NASA Astrophysics Data System (ADS)

    Fuchs, Alexander; Pengel, Steffen; Bergmeier, Jan; Kahrs, Lüder A.; Ortmaier, Tobias

    2015-07-01

    Laser surgery is an established clinical procedure in dental applications, soft tissue ablation, and ophthalmology. The presented experimental set-up for closed-loop control of laser bone ablation addresses a feedback system and enables safe ablation towards anatomical structures that usually would have high risk of damage. This study is based on combined working volumes of optical coherence tomography (OCT) and Er:YAG cutting laser. High level of automation in fast image data processing and tissue treatment enables reproducible results and shortens the time in the operating room. For registration of the two coordinate systems a cross-like incision is ablated with the Er:YAG laser and segmented with OCT in three distances. The resulting Er:YAG coordinate system is reconstructed. A parameter list defines multiple sets of laser parameters including discrete and specific ablation rates as ablation model. The control algorithm uses this model to plan corrective laser paths for each set of laser parameters and dynamically adapts the distance of the laser focus. With this iterative control cycle consisting of image processing, path planning, ablation, and moistening of tissue the target geometry and desired depth are approximated until no further corrective laser paths can be set. The achieved depth stays within the tolerances of the parameter set with the smallest ablation rate. Specimen trials with fresh porcine bone have been conducted to prove the functionality of the developed concept. Flat bottom surfaces and sharp edges of the outline without visual signs of thermal damage verify the feasibility of automated, OCT controlled laser bone ablation with minimal process time.

  1. Wave-packet squeezing by iterative pump-dump control in diatomic molecules

    SciTech Connect

    Chang, Bo Y.; Lee, Sungyul; Sola, Ignacio R.; Santamaria, Jesus

    2006-02-15

    Iterative vibrational wave packet squeezing by a sequence of femtosecond pulses is proposed. Analytic formulas are derived for the harmonic oscillator case, and the practical implementation of the scheme is tested on different electronic transitions in Rb{sub 2}.

  2. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    NASA Astrophysics Data System (ADS)

    Durodié, Frédéric; Dumortier, Pierre; Helou, Walid; Křivská, Alena; Lerche, Ernesto

    2015-12-01

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  3. Circuit model of the ITER-like antenna for JET and simulation of its control algorithms

    SciTech Connect

    Durodié, Frédéric Křivská, Alena; Helou, Walid; Collaboration: EUROfusion Consortium

    2015-12-10

    The ITER-like Antenna (ILA) for JET [1] is a 2 toroidal by 2 poloidal array of Resonant Double Loops (RDL) featuring in-vessel matching capacitors feeding RF current straps in conjugate-T manner, a low impedance quarter-wave impedance transformer, a service stub allowing hydraulic actuator and water cooling services to reach the aforementioned capacitors and a 2nd stage phase-shifter-stub matching circuit allowing to correct/choose the conjugate-T working impedance. Toroidally adjacent RDLs are fed from a 3dB hybrid splitter. It has been operated at 33, 42 and 47MHz on plasma (2008-2009) while it presently estimated frequency range is from 29 to 49MHz. At the time of the design (2001-2004) as well as the experiments the circuit models of the ILA were quite basic. The ILA front face and strap array Topica model was relatively crude and failed to correctly represent the poloidal central septum, Faraday Screen attachment as well as the segmented antenna central septum limiter. The ILA matching capacitors, T-junction, Vacuum Transmission Line (VTL) and Service Stubs were represented by lumped circuit elements and simple transmission line models. The assessment of the ILA results carried out to decide on the repair of the ILA identified that achieving routine full array operation requires a better understanding of the RF circuit, a feedback control algorithm for the 2nd stage matching as well as tighter calibrations of RF measurements. The paper presents the progress in modelling of the ILA comprising a more detailed Topica model of the front face for various plasma Scrape Off Layer profiles, a comprehensive HFSS model of the matching capacitors including internal bellows and electrode cylinders, 3D-EM models of the VTL including vacuum ceramic window, Service stub, a transmission line model of the 2nd stage matching circuit and main transmission lines including the 3dB hybrid splitters. A time evolving simulation using the improved circuit model allowed to design and

  4. Learning arm's posture control using reinforcement learning and feedback-error-learning.

    PubMed

    Kambara, H; Kim, J; Sato, M; Koike, Y

    2004-01-01

    In this paper, we propose a learning model using the Actor-Critic method and the feedback-error-learning scheme. The Actor-Critic method, which is one of the major frameworks in reinforcement learning, has attracted attention as a computational learning model in the basal ganglia. Meanwhile, the feedback-error-learning is learning architecture proposed as a computationally coherent model of cerebellar motor learning. This learning architecture's purpose is to acquire a feed-forward controller by using a feedback controller's output as an error signal. In past researches, a predetermined constant gain feedback controller was used for the feedback-error-learning. We use the Actor-Critic method for obtaining a feedback controller in the feedback-error-earning. By applying the proposed learning model to an arm's posture control, we show that high-performance feedback and feed-forward controller can be acquired from only by using a scalar value of reward. PMID:17271719

  5. Non-iterative adaptive time-stepping scheme with temporal truncation error control for simulating variable-density flow

    NASA Astrophysics Data System (ADS)

    Hirthe, Eugenia M.; Graf, Thomas

    2012-12-01

    The automatic non-iterative second-order time-stepping scheme based on the temporal truncation error proposed by Kavetski et al. [Kavetski D, Binning P, Sloan SW. Non-iterative time-stepping schemes with adaptive truncation error control for the solution of Richards equation. Water Resour Res 2002;38(10):1211, http://dx.doi.org/10.1029/2001WR000720.] is implemented into the code of the HydroGeoSphere model. This time-stepping scheme is applied for the first time to the low-Rayleigh-number thermal Elder problem of free convection in porous media [van Reeuwijk M, Mathias SA, Simmons CT, Ward JD. Insights from a pseudospectral approach to the Elder problem. Water Resour Res 2009;45:W04416, http://dx.doi.org/10.1029/2008WR007421.], and to the solutal [Shikaze SG, Sudicky EA, Schwartz FW. Density-dependent solute transport in discretely-fractured geological media: is prediction possible? J Contam Hydrol 1998;34:273-91] problem of free convection in fractured-porous media. Numerical simulations demonstrate that the proposed scheme efficiently limits the temporal truncation error to a user-defined tolerance by controlling the time-step size. The non-iterative second-order time-stepping scheme can be applied to (i) thermal and solutal variable-density flow problems, (ii) linear and non-linear density functions, and (iii) problems including porous and fractured-porous media.

  6. Vehicle Steering control: A model of learning

    NASA Technical Reports Server (NTRS)

    Smiley, A.; Reid, L.; Fraser, M.

    1978-01-01

    A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

  7. Indirect learning control for nonlinear dynamical systems

    NASA Technical Reports Server (NTRS)

    Ryu, Yeong Soon; Longman, Richard W.

    1993-01-01

    In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

  8. Enhanced Confinement Scenarios Without Large Edge Localized Modes in Tokamaks: Control, Performance, and Extrapolability Issues for ITER

    SciTech Connect

    Maingi, R

    2014-07-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. The two baseline strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R & D. In addition, recent progress in ELM-free regimes, namely Quiescent H-mode, I-mode, and Enhanced Pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  9. Enhanced confinement scenarios without large edge localized modes in tokamaks: control, performance, and extrapolability issues for ITER

    NASA Astrophysics Data System (ADS)

    Maingi, R.

    2014-11-01

    Large edge localized modes (ELMs) typically accompany good H-mode confinement in fusion devices, but can present problems for plasma facing components because of high transient heat loads. Here the range of techniques for ELM control deployed in fusion devices is reviewed. Two strategies in the ITER baseline design are emphasized: rapid ELM triggering and peak heat flux control via pellet injection, and the use of magnetic perturbations to suppress or mitigate ELMs. While both of these techniques are moderately well developed, with reasonable physical bases for projecting to ITER, differing observations between multiple devices are also discussed to highlight the needed community R&D. In addition, recent progress in ELM-free regimes, namely quiescent H-mode, I-mode, and enhanced pedestal H-mode is reviewed, and open questions for extrapolability are discussed. Finally progress and outstanding issues in alternate ELM control techniques are reviewed: supersonic molecular beam injection, edge electron cyclotron heating, lower hybrid heating and/or current drive, controlled periodic jogs of the vertical centroid position, ELM pace-making via periodic magnetic perturbations, ELM elimination with lithium wall conditioning, and naturally occurring small ELM regimes.

  10. Learning styles: The learning methods of air traffic control students

    NASA Astrophysics Data System (ADS)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  11. Version Control in Project-Based Learning

    ERIC Educational Resources Information Center

    Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

    2008-01-01

    This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

  12. A Nonlinear Learning Controller for Robotic Manipulators

    NASA Astrophysics Data System (ADS)

    Miller, W. Thomas

    1987-03-01

    A practical learning control system is described which is applicable to complex robotic systems involving multiple feedback sensors and multiple command variables during both repetitive and nonrepetitive operations. The learning algorithm utilizes the Cerebellar Model Arithmetic Computer (CMAC) neural model developed by Albus. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is then used to predict the command signals required to produce desired changes in the sensor outputs. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved learning to use video image feedback to track three dimensional task trajectories relative to objects moving on a conveyor. No a priori knowledge of the robot kinematics or of the conveyor speed or orientation relative to the robot was assumed. In all experiments, control system tracking error was found to converge after a few trials to within error limits defined by the resolution of the sensor feedback data.

  13. Feedback Error Learning in neuromotor control

    NASA Astrophysics Data System (ADS)

    Ishihara, Abraham K.

    This thesis is concerned with adaptive human motor control. Adaptation is a highly desirable characteristic of any biological system. Failure is an undesirable, yet very real, characteristic of the human motor control systems. Variability is a ubiquitous observation in human movements that has no direct analogue in the design and analysis of robotic control algorithms. This thesis attempts to link these three aspects of motor control under the constraints of a biologically inspired control framework termed Feedback Error Learning (FEL). Utilizing nonlinear and adaptive control methods we prove conditions for which the FEL framework is stable and successful learning can occur. Utilizing singular perturbation methods, we derive conditions for which the system is guaranteed to fail. Variability is analyzed using Ito Calculus and stochastic Lyapunov functionals where signal dependent noise, a commonly observed phenomenon, enters in the learning algorithm. We also show how signal dependent noise might benefit biological control systems despite the inherent variability introduced into the motor control loops. Lastly, we investigate a force tracking control task, where subjects are asked to track a time-varying plant. Using basic control and system identification techniques, we probe the human motor learning system and extract learning rates with respect to the FEL model.

  14. Grounding cognitive control in associative learning.

    PubMed

    Abrahamse, Elger; Braem, Senne; Notebaert, Wim; Verguts, Tom

    2016-07-01

    Cognitive control covers a broad range of cognitive functions, but its research and theories typically remain tied to a single domain. Here we outline and review an associative learning perspective on cognitive control in which control emerges from associative networks containing perceptual, motor, and goal representations. Our review identifies 3 trending research themes that are shared between the domains of conflict adaptation, task switching, response inhibition, and attentional control: Cognitive control is context-specific, can operate in the absence of awareness, and is modulated by reward. As these research themes can be envisaged as key characteristics of learning, we propose that their joint emergence across domains is not coincidental but rather reflects a (latent) growth of interest in learning-based control. Associative learning has the potential for providing broad-scaled integration to cognitive control theory, and offers a promising avenue for understanding cognitive control as a self-regulating system without postulating an ill-defined set of homunculi. We discuss novel predictions, theoretical implications, and immediate challenges that accompany an associative learning perspective on cognitive control. (PsycINFO Database Record PMID:27148628

  15. Safety-factor profile tailoring by improved electron cyclotron system for sawtooth control and reverse shear scenarios in ITER

    SciTech Connect

    Zucca, C.; Sauter, O.; Fable, E.; Henderson, M. A.; Polevoi, A.; Saibene, G.

    2008-11-01

    The effect of the predicted local electron cyclotron current driven by the optimized electron cyclotron system on ITER is discussed. A design variant was recently proposed to enlarge the physics program covered by the upper and equatorial launchers. By extending the functionality range of the upper launcher, significant control capabilities of the sawtooth period can be obtained. The upper launcher improvement still allows enough margin to exceed the requirements for neoclassical tearing mode stabilization, for which it was originally designed. The analysis of the sawtooth control is carried on with the ASTRA transport code, coupled with the threshold model by Por-celli, to study the control capabilities of the improved upper launcher on the sawtooth instability. The simulations take into account the significant stabilizing effect of the fusion alpha particles. The sawtooth period can be increased by a factor of 1.5 with co-ECCD outside the q = 1 surface, and decreased by at least 30% with co-ECCD inside q = 1. The present ITER base-line design has the electron cyclotron launchers providing only co-ECCD. The variant for the equatorial launcher proposes the possibility to drive counter-ECCD with 1 of the 3 rows of mirrors: the counter-ECCD can then be balanced with co-ECCD and provide pure ECH with no net driven current. The difference between full co-ECCD off-axis using all 20MW from the equatorial launcher and 20MW co-ECCD driven by 2/3 from the equatorial launcher and 1/3 from the upper launcher is shown to be negligible. Cnt-ECCD also offers greater control of the plasma current density, therefore this analysis addresses the performance of the equatorial launcher to control the central q profile. The equatorial launcher is shown to control very efficiently the value of q{sub 0.2}-q{sub min} in advanced scenarios, if one row provides counter-ECCD.

  16. Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning

    NASA Astrophysics Data System (ADS)

    Zhang, Jilie; Zhang, Huaguang; Wang, Binrui; Cai, Tiaoyang

    2016-05-01

    In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix ? in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.

  17. An iterative particle filter approach for coupled hydro-geophysical inversion of a controlled infiltration experiment

    SciTech Connect

    Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario

    2015-02-15

    The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

  18. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  19. Learning to Control Advanced Life Support Systems

    NASA Technical Reports Server (NTRS)

    Subramanian, Devika

    2004-01-01

    Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

  20. A learning controller for nonrepetitive robotic operation

    NASA Technical Reports Server (NTRS)

    Miller, W. T., III

    1987-01-01

    A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

  1. Model-free constrained data-driven iterative reference input tuning algorithm with experimental validation

    NASA Astrophysics Data System (ADS)

    Radac, Mircea-Bogdan; Precup, Radu-Emil

    2016-05-01

    This paper presents the design and experimental validation of a new model-free data-driven iterative reference input tuning (IRIT) algorithm that solves a reference trajectory tracking problem as an optimization problem with control signal saturation constraints and control signal rate constraints. The IRIT algorithm design employs an experiment-based stochastic search algorithm to use the advantages of iterative learning control. The experimental results validate the IRIT algorithm applied to a non-linear aerodynamic position control system. The results prove that the IRIT algorithm offers the significant control system performance improvement by few iterations and experiments conducted on the real-world process and model-free parameter tuning.

  2. Discrete time learning control in nonlinear systems

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Chang, Chi-Kuang; Phan, Minh

    1992-01-01

    In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

  3. Dynamic optimization of chemical engineering problems using a control vector parameterization method with an iterative genetic algorithm

    NASA Astrophysics Data System (ADS)

    Qian, Feng; Sun, Fan; Zhong, Weimin; Luo, Na

    2013-09-01

    An approach that combines genetic algorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, control variables are approximated with polynomials based on state variables and time in the entire time interval. The iterative method, which reduces redundant expense and improves computing efficiency, is used with GA to reduce the width of the search region. Constrained dynamic optimization problems are even more difficult. A new method that embeds the information of infeasible chromosomes into the evaluation function is introduced in this study to solve dynamic optimization problems with or without constraint. The results demonstrated the feasibility and robustness of the proposed methods. The proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is not available.

  4. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  5. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    NASA Astrophysics Data System (ADS)

    Wei, Qing-Lai; Song, Rui-Zhuo; Sun, Qiu-Ye; Xiao, Wen-Dong

    2015-09-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. Project supported by the National Natural Science Foundation of China (Grant Nos. 61304079 and 61374105), the Beijing Natural Science Foundation, China (Grant Nos. 4132078 and 4143065), the China Postdoctoral Science Foundation (Grant No. 2013M530527), the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-119A2), and the Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20150104).

  6. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    PubMed

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer.

  7. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

    PubMed

    Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

    2012-09-01

    Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer. PMID:22633781

  8. Measuring strategic control in artificial grammar learning.

    PubMed

    Norman, Elisabeth; Price, Mark C; Jones, Emma

    2011-12-01

    In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning.

  9. A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy HDP iteration algorithm.

    PubMed

    Zhang, Huaguang; Wei, Qinglai; Luo, Yanhong

    2008-08-01

    In this paper, we aim to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using the greedy heuristic dynamic programming (HDP) iteration algorithm. A new type of performance index is defined because the existing performance indexes are very difficult in solving this kind of tracking problem, if not impossible. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then, the greedy HDP iteration algorithm is introduced to deal with the regulation problem with rigorous convergence analysis. Three neural networks are used to approximate the performance index, compute the optimal control policy, and model the nonlinear system for facilitating the implementation of the greedy HDP iteration algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.

  10. Amygdala microcircuits controlling learned fear.

    PubMed

    Duvarci, Sevil; Pare, Denis

    2014-06-01

    We review recent work on the role of intrinsic amygdala networks in the regulation of classically conditioned defensive behaviors, commonly known as conditioned fear. These new developments highlight how conditioned fear depends on far more complex networks than initially envisioned. Indeed, multiple parallel inhibitory and excitatory circuits are differentially recruited during the expression versus extinction of conditioned fear. Moreover, shifts between expression and extinction circuits involve coordinated interactions with different regions of the medial prefrontal cortex. However, key areas of uncertainty remain, particularly with respect to the connectivity of the different cell types. Filling these gaps in our knowledge is important because much evidence indicates that human anxiety disorders results from an abnormal regulation of the networks supporting fear learning.

  11. Design of an iterative auto-tuning algorithm for a fuzzy PID controller

    NASA Astrophysics Data System (ADS)

    Saeed, Bakhtiar I.; Mehrdadi, B.

    2012-05-01

    Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

  12. Predictive and reinforcement learning for magneto-hydrodynamic control of hypersonic flows

    NASA Astrophysics Data System (ADS)

    Kulkarni, Nilesh Vijay

    Increasing needs for autonomy in future aerospace systems and immense progress in computing technology have motivated the development of on-line adaptive control techniques to account for modeling errors, changes in system dynamics, and faults occurring during system operation. After extensive treatment of the inner-loop adaptive control dealing mainly with stable adaptation towards desired transient behavior, adaptive optimal control has started receiving attention in literature. Motivated by the problem of optimal control of the magneto-hydrodynamic (MHD) generator at the inlet of the scramjet engine of a hypersonic flight vehicle, this thesis treats the general problem of efficiently combining off-line and on-line optimal control methods. The predictive control approach is chosen as the off-line method for designing optimal controllers using all the existing system knowledge. This controller is then adapted on-line using policy-iteration-based Q-learning, which is a stable model-free reinforcement learning approach. The combined approach is first illustrated in the optimal control of linear systems, which helps in the analysis as well as the validation of the method. A novel neural-networks-based parametric predictive control approach is then designed for the off-line optimal control of non-linear systems. The off-line approach is illustrated by applications to aircraft and spacecraft systems. This is followed by an extensive treatment of the off-line optimal control of the MHD generator using this neuro-control approach. On-line adaptation of the controller is implemented using several novel schemes derived from the policy-iteration-based Q-learning. The implementation results demonstrate the success of these on-line algorithms for adapting towards modeling errors in the off-line design.

  13. Fusion Physics Toward ITER

    NASA Astrophysics Data System (ADS)

    Stambaugh, R. D.

    2006-04-01

    Stars are powered by fusion, the energy released by fusing together light nuclei, using gravitational confinement of plasma. Fusion on earth will be done in a 100 million degree plasma made of deuterium and tritium and confined by magnetic fields or inertia. The worldwide fusion research community will construct ITER, the first experiment that will burn a DT plasma by copious fusion reactions. ITER's nominal goal is to create 500 MW of fusion power. An energy gain of 10 will mean the plasma is dominantly self-heated by the fusion-produced alpha particles. ITER's all superconducting magnet technology and steady-state heat removal technology will enable nominal 400 s pulses to allow the study of burning plasmas on the longest intrinsic timescale of the confined plasma - diffusive redistribution of the electrical currents in the plasma. The advances in magnetic confinement physics that have led to this opportunity will be described, as well as the research opportunities afforded by ITER. The physics of confining stable plasmas and heating them will produce the high gain state in ITER. Sustained burn will come from the physics of controlling currents in plasmas and how the hot plasma is interfaced to its room temperature surroundings. ITER will provide our first experience with how fusion plasma self-heating will profoundly affect the complex, interlinked physical processes that occur in confined plasmas.

  14. Nonlinear control and online optimization of the burn condition in ITER via heating, isotopic fueling and impurity injection

    NASA Astrophysics Data System (ADS)

    Boyer, Mark D.; Schuster, Eugenio

    2014-10-01

    The ITER tokamak, the next experimental step toward the development of nuclear fusion reactors, will explore the burning plasma regime in which the plasma temperature is sustained mostly by fusion heating. Regulation of the fusion power through modulation of fueling and external heating sources, referred to as burn control, is one of the fundamental problems in burning plasma research. Active control will be essential for achieving and maintaining desired operating points, responding to changing power demands, and ensuring stable operation. Most existing burn control efforts use either non-model-based control techniques or designs based on linearized models. These approaches must be designed for particular operating points and break down for large perturbations. In this work, we utilize a spatially averaged (zero-dimensional) nonlinear model to synthesize a multi-variable nonlinear burn control strategy that can reject large perturbations and move between operating points. The controller uses all of the available actuation techniques in tandem to ensure good performance, even if one or more of the actuators saturate. Adaptive parameter estimation is used to improve the model parameter estimates used by the feedback controller in real-time and ensure asymptotic tracking of the desired operating point. In addition, we propose the use of a model-based online optimization algorithm to drive the system to a state that minimizes a given cost function, while respecting input and state constraints. A zero-dimensional simulation study is presented to show the performance of the adaptive control scheme and the optimization scheme with a cost function weighting the fusion power and temperature tracking errors.

  15. Model learning for robot control: a survey.

    PubMed

    Nguyen-Tuong, Duy; Peters, Jan

    2011-11-01

    Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

  16. Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

    PubMed

    Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

    2014-01-01

    It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

  17. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    NASA Astrophysics Data System (ADS)

    Messiaen, A.; Swain, D.; Ongena, J.; Vervier, M.

    2015-12-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode Vmax amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of Vmax of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k// computed by means of the coupling code ANTITER II remains small for the considered cases.

  18. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, A. Ongena, J.; Vervier, M.; Swain, D.

    2015-12-10

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V{sub max} amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V{sub max} of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is ±20°, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k{sub //} computed by means of the coupling code ANTITER II remains small for the considered cases.

  19. Analysis of the phase control of the ITER ICRH antenna array. Influence on the load resilience and radiated power spectrum

    SciTech Connect

    Messiaen, Andre; Swain, David W; Ongena, Jef; Vervier, Michael

    2015-01-01

    The paper analyses how the phasing of the ITER ICRH 24 strap array evolves from the power sources up to the strap currents of the antenna. The study of the phasing control and coherence through the feeding circuits with prematching and automatic matching and decoupling network is made by modeling starting from the TOPICA matrix of the antenna array for a low coupling plasma profile and for current drive phasing (worst case for mutual coupling effects). The main results of the analysis are: (i) the strap current amplitude is well controlled by the antinode V-max amplitude of the feeding lines, (ii) the best toroidal phasing control is done by the adjustment of the mean phase of V-max of each poloidal straps column, (iii) with well adjusted system the largest strap current phasing error is +/- 20 degrees, (iv) the effect on load resilience remains well below the maximum affordable VSWR of the generators, (v) the effect on the radiated power spectrum versus k//computed by means of the coupling code ANTITER II remains small for the considered cases. [GRAPHICS] .

  20. Reinforcement learning output feedback NN control using deterministic learning technique.

    PubMed

    Xu, Bin; Yang, Chenguang; Shi, Zhongke

    2014-03-01

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control. PMID:24807456

  1. Reinforcement learning output feedback NN control using deterministic learning technique.

    PubMed

    Xu, Bin; Yang, Chenguang; Shi, Zhongke

    2014-03-01

    In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control.

  2. A Case Study of Modern PLC and LabVIEW Controls: Power Supply Controls for the ORNL ITER ECH Test Stand

    SciTech Connect

    Barker, Alan M; Killough, Stephen M; Bigelow, Tim S; White, John A; Munro Jr, John K

    2011-01-01

    Power Supply Controls are being developed at Oak Ridge National Laboratory (ORNL) to test transmission line components of the Electron Cyclotron Heating (ECH) system, with a focus on gyrotrons and waveguides, in support of the International Thermonuclear Experimental Reactor (ITER). The control is performed by several Programmable Logic Controllers (PLC s) located near the different equipment. A technique of Supervisory Control and Data Acquisition (SCADA) is presented to monitor, control, and log actions of the PLC s on a PC through use of Allen Bradley s Remote I/O communication interface coupled with an Open Process Control/Object Linking and Embedding [OLE] for Process Control (OPC) Server/Client architecture. The OPC data is then linked to a National Instruments (NI) LabVIEW system for monitoring and control. Details of the architecture and insight into applicability to other systems are presented in the rest of this paper. Future integration with an EPICS (Experimental Physics Industrial Control System) based mini-CODAC (Control, Data Access and Communication) SCADA system is under consideration, and integration considerations will be briefly introduced.

  3. Connectionist reinforcement learning of robot control skills

    NASA Astrophysics Data System (ADS)

    Araújo, Rui; Nunes, Urbano; de Almeida, A. T.

    1998-07-01

    Many robot manipulator tasks are difficult to model explicitly and it is difficult to design and program automatic control algorithms for them. The development, improvement, and application of learning techniques taking advantage of sensory information would enable the acquisition of new robot skills and avoid some of the difficulties of explicit programming. In this paper we use a reinforcement learning approach for on-line generation of skills for control of robot manipulator systems. Instead of generating skills by explicit programming of a perception to action mapping they are generated by trial and error learning, guided by a performance evaluation feedback function. The resulting system may be seen as an anticipatory system that constructs an internal representation model of itself and of its environment. This enables it to identify its current situation and to generate corresponding appropriate commands to the system in order to perform the required skill. The method was applied to the problem of learning a force control skill in which the tool-tip of a robot manipulator must be moved from a free space situation, to a contact state with a compliant surface and having a constant interaction force.

  4. Air pollution control system research: An iterative approach to developing affordable systems

    NASA Technical Reports Server (NTRS)

    Watt, Lewis C.; Cannon, Fred S.; Heinsohn, Robert J.; Spaeder, Timothy A.

    1995-01-01

    This paper describes a Strategic Environmental Research and Development Program (SERDP) funded project led jointly by the Marine Corps Multi-Commodity Maintenance Centers, and the Air and Energy Engineering Research Laboratory (AEERL) of the USEPA. The research focuses on paint booth exhaust minimization using recirculation, and on volatile organic compound (VOC) oxidation by the modules of a hybrid air pollution control system. The research team is applying bench, pilot and full scale systems to accomplish the goals of reduced cost and improved effectiveness of air treatment systems for paint booth exhaust.

  5. Air pollution control system research: An iterative approach to developing affordable systems

    SciTech Connect

    Watt, L.C.; Cannon, F.S.; Heinsohn, R.J.; Spaeder, T.A.; Darvin, C.H.

    1993-12-31

    The research will be accomplished on lab scale, pilot scale, and production air pollution control systems (APCS). The production system, to be installed at Marine Corps Logistics Base (MCLB) Barstow, CA, will treat the exhaust from three paint booths which will be modified to recirculate a large percentage of their exhaust. These recirculation systems are, themselves, a critical element in the overall R and D effort. The goal of the program is to conduct an R and D effort which will improve and demonstrate a combination of technologies intended to make VOC treatment both effective and affordable. The US Marine Corps, the other services and industry will each benefit.

  6. X-29 flight control system: Lessons learned

    NASA Technical Reports Server (NTRS)

    Clarke, Robert; Burken, John J.; Bosworth, John T.; Bauer, Jeffrey E.

    1995-01-01

    Two X-29A aircraft were flown at the NASA Dryden Flight Research Center over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35-percent negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low- and high-angle-of-attack programs. Throughout the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

  7. X-29 flight control system: Lessons learned

    NASA Technical Reports Server (NTRS)

    Clarke, Robert; Burken, John J.; Bosworth, John T.; Bauer, Jeffery E.

    1994-01-01

    Two X-29A aircraft were flown at the NASA Dryden Flight Research Center over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35-percent negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low- and high-angle-of-attack programs. Through out the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

  8. Physics and control of ELMing H-mode negative-central-shear advanced tokamak ITER scenario based on experimental profiles from DIII-D

    NASA Astrophysics Data System (ADS)

    Lao, L. L.; Chan, V. S.; Chu, M. S.; Evans, T.; Humphreys, D. A.; Leuer, J. A.; Mahdavi, M. A.; Petrie, T. W.; Snyder, P. B.; St. John, H. E.; Staebler, G. M.; Stambaugh, R. D.; Taylor, T. S.; Turnbull, A. D.; West, W. P.; Brennan, D. P.

    2003-10-01

    Key DIII-D advanced tokamak (AT) experimental and modelling results are applied to examine the physics and control issues for ITER to operate in a negative central shear (NCS) AT scenario. The effects of a finite edge pressure pedestal and current density are included based on the DIII-D experimental profiles. Ideal and resistive stability analyses demonstrate that feedback control of resistive wall modes by rotational drive or flux conserving intelligent coils is crucial for these AT configurations to operate at attractive bgrN values in the range 3.0-3.5. Vertical stability and halo current analyses show that reliable disruption mitigation is essential and mitigation control using an impurity gas can significantly reduce the local mechanical stress to an acceptable level. Core transport and turbulence analyses indicate that control of the rotational shear profile is essential to reduce the pedestal temperature required for high bgr. Consideration of edge stability and core transport suggests that a sufficiently wide pedestal is necessary for the projected fusion performance. Heat flux analyses indicate that, with core-only radiation enhancement, the outboard peak divertor heat load is near the design limit of 10 MW m-2. Detached operation may be necessary to reduce the heat flux to a more manageable level. Evaluation of the ITER pulse length using a local step response approach indicates that the 3000 s ITER long-pulse scenario is probably both necessary and sufficient for demonstration of local current profile control.

  9. Heritability of motor control and motor learning

    PubMed Central

    Missitzi, Julia; Gentner, Reinhard; Misitzi, Angelica; Geladas, Nickos; Politis, Panagiotis; Klissouras, Vassilis; Classen, Joseph

    2013-01-01

    Abstract The aim of this study was to elucidate the relative contribution of genes and environment on individual differences in motor control and acquisition of a force control task, in view of recent association studies showing that several candidate polymorphisms may have an effect on them. Forty‐four healthy female twins performed brisk isometric abductions with their right thumb. Force was recorded by a transducer and fed back to the subject on a computer screen. The task was to place the tracing of the peak force in a force window defined between 30% and 40% of the subject's maximum force, as determined beforehand. The initial level of proficiency was defined as the number of attempts reaching the force window criterion within the first 100 trials. The difference between the number of successful trials within the last and the first 100 trials was taken as a measure of motor learning. For motor control, defined by the initial level of proficiency, the intrapair differences in monozygotic (MZ) and dizygotic (DZ) twins were 6.8 ± 7.8 and 13.8 ± 8.4, and the intrapair correlations 0.77 and 0.39, respectively. Heritability was estimated at 0.68. Likewise for motor learning intrapair differences in the increment of the number of successful trials in MZ and DZ twins were 5.4 ± 5.2 and 12.8 ± 7, and the intrapair correlations 0.58 and 0.19. Heritability reached 0.70. The present findings suggest that heredity accounts for a major part of existing differences in motor control and motor learning, but uncertainty remains which gene polymorphisms may be responsible. PMID:24744865

  10. Tunnel Ventilation Control Using Reinforcement Learning Methodology

    NASA Astrophysics Data System (ADS)

    Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

    The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

  11. Space Station Control Moment Gyroscope Lessons Learned

    NASA Technical Reports Server (NTRS)

    Gurrisi, Charles; Seidel, Raymond; Dickerson, Scott; Didziulis, Stephen; Frantz, Peter; Ferguson, Kevin

    2010-01-01

    Four 4760 Nms (3510 ft-lbf-s) Double Gimbal Control Moment Gyroscopes (DGCMG) with unlimited gimbal freedom about each axis were adopted by the International Space Station (ISS) Program as the non-propulsive solution for continuous attitude control. These CMGs with a life expectancy of approximately 10 years contain a flywheel spinning at 691 rad/s (6600 rpm) and can produce an output torque of 258 Nm (190 ft-lbf)1. One CMG unexpectedly failed after approximately 1.3 years and one developed anomalous behavior after approximately six years. Both units were returned to earth for failure investigation. This paper describes the Space Station Double Gimbal Control Moment Gyroscope design, on-orbit telemetry signatures and a summary of the results of both failure investigations. The lessons learned from these combined sources have lead to improvements in the design that will provide CMGs with greater reliability to assure the success of the Space Station. These lessons learned and design improvements are not only applicable to CMGs but can be applied to spacecraft mechanisms in general.

  12. Learning Dynamic Control of Body Roll Orientation

    PubMed Central

    Vimal, Vivekanand Pandey; Lackner, James R.; DiZio, Paul

    2016-01-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n=10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system device (MARS) programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30 ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/sec2. Each subject participated in 5 blocks of 4 trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently. PMID:26525709

  13. Learning dynamic control of body roll orientation.

    PubMed

    Vimal, Vivekanand Pandey; Lackner, James R; DiZio, Paul

    2016-02-01

    Our objective was to examine how the control of orientation is learned in a task involving dynamically balancing about an unstable equilibrium point, the gravitational vertical, in the absence of leg reflexes and muscle stiffness. Subjects (n = 10) used a joystick to set themselves to the gravitational vertical while seated in a multi-axis rotation system (MARS) device programmed with inverted pendulum dynamics. The MARS is driven by powerful servomotors and can faithfully follow joystick commands up to 2.5 Hz with a 30-ms latency. To make the task extremely difficult, the pendulum constant was set to 600°/s(2). Each subject participated in five blocks of four trials, with a trial ending after a cumulative 100 s of balancing, excluding reset times when a subject lost control. To characterize performance and learning, we used metrics derived from joystick movements, phase portraits (joystick deflections vs MARS position and MARS velocity vs angular position), and stabilogram diffusion functions. We found that as subjects improved their balancing performance, they did so by making fewer destabilizing joystick movements and reducing the number and duration of joystick commands. The control strategy they acquired involved making more persistent short-term joystick movements, waiting longer before making changes to ongoing motion, and only intervening intermittently.

  14. Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

    NASA Technical Reports Server (NTRS)

    Niewoehner, Kevin R.; Carter, John (Technical Monitor)

    2001-01-01

    The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

  15. Locus of Control and Learning Disabilities: A Review and Discussion.

    ERIC Educational Resources Information Center

    Dudley-Marling, Curtis C.; And Others

    1982-01-01

    A literature review reveals that learning disabled children are more likely than normal achievers to attribute successes, but not failures, to external factors. The implications of locus of control for the field of learning disabilities are discussed in terms of its relation to academic achievement, learned helplessness, and remediation programs.…

  16. Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

    ERIC Educational Resources Information Center

    Peek, Ron

    2016-01-01

    By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

  17. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  18. A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

    NASA Technical Reports Server (NTRS)

    Callini, Gianluca

    2016-01-01

    With a brand new fire set ablaze by a serendipitous convergence of events ranging from a science fiction novel and movie ("The Martian"), to ground-breaking recent discoveries of flowing water on its surface, the drive for the journey to Mars seems to be in a higher gear than ever before. We are developing new spacecraft and support systems to take humans to the Red Planet, while scientists on Earth continue using the International Space Station as a laboratory to evaluate the effects of long duration space flight on the human body. Written from the perspective of a facility test director rather than a researcher, and using past and current life support systems tests as examples, this paper seeks to provide an overview on how facility teams approach testing, the kind of information they need to ensure efficient collaborations and successful tests, and how, together with researchers and principal investigators, we can collectively apply what we learn to execute future tests.

  19. Iterative Learning of Transcatheter Mitral Valve Replacement in Mitral Valve Annulus Calcification: Management and Prevention of Transcatheter Mitral Valve Replacement Dislocation.

    PubMed

    Hulman, Michal; Bena, Martin; Artemiou, Panagiotis; Gasparovic, Ivo; Hudec, Vladan; Rajani, Ronak; Bapat, Vinayak

    2016-10-01

    Transcatheter mitral valve replacement using balloon-expandable valves is an emerging technique for the treatment of patients with significant mitral regurgitation who have been judged to be inoperable owing to significant mitral valve annulus calcification. Although initial reports have been promising, there remains a lack of consensus as to how to plan for transcatheter mitral valve replacement deployment in terms of appropriateness, sizing, and positioning to mitigate the risks of valve displacement and paravalvular regurgitation. We describe two cases of transcatheter mitral valve replacement in patients with significant mitral valve annulus calcification. The first was complicated by valve displacement into the left atrium, which was successfully managed by surgical redeployment and fixation. The second case was thereafter performed successfully using iterative learning and the application of specific preprocedural planning techniques acquired from a root cause analysis of the first case. We describe our experience with both cases and the specific planning principles required to prevent transcatheter mitral valve replacement displacement in patients with mitral valve annulus calcification. PMID:27645964

  20. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    SciTech Connect

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  1. Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

    PubMed

    Liu, Derong; Wang, Ding; Li, Hongliang

    2014-02-01

    In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme. PMID:24807039

  2. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  3. Feedback control by online learning an inverse model.

    PubMed

    Waegeman, Tim; Wyffels, Francis; Schrauwen, Francis

    2012-10-01

    A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made. PMID:24808008

  4. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  5. Communal learning within a distributed robotic control system

    NASA Astrophysics Data System (ADS)

    Digney, Bruce L.

    2001-09-01

    It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

  6. Learning hybrid force and position control of robot manipulators

    SciTech Connect

    Jeon, Doyoung |; Tomizuka, Masayoshi

    1993-08-01

    When a robot performs the same task repeatedly, a learning controller can enhance the performance of the system significantly. The learning control, however, has not been studied in the force control of robot manipulators as extensively as in the position control of robot manipulators. In this paper, the learning control is applied to hybrid force and position control of robot manipulators. When the geometry and position of a constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhances the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be asymptotically stable. A two degree of freedom SCARA-type direct-drive robot manipulator is used to test the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N (within the root-mean-square force error of 1.95 N) and with a desired tangential velocity. The experiments confirmed the effectiveness of the learning hybrid force and position controller.

  7. US ITER Moving Forward

    ScienceCinema

    US ITER / ORNL

    2016-07-12

    US ITER Project Manager Ned Sauthoff, joined by Wayne Reiersen, Team Leader Magnet Systems, and Jan Berry, Team Leader Tokamak Cooling System, discuss the U.S.'s role in the ITER international collaboration.

  8. Efficient model learning methods for actor-critic control.

    PubMed

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm. PMID:22156998

  9. An Iterative Solution to the Nonlinear Time-Discrete TEM Model - The Occurrence of Chaos and a Control Theoretic Algorithmic Approach

    NASA Astrophysics Data System (ADS)

    Pickl, S.

    2002-09-01

    This paper is concerned with a mathematical derivation of the nonlinear time-discrete Technology-Emissions Means (TEM-) model. A detailed introduction to the dynamics modelling a Joint Implementation Program concerning Kyoto Protocol is given at the end of the paper. As the nonlinear time-discrete dynamics tends to chaotic behaviour, the necessary introduction of control parameters in the dynamics of the TEM model leads to new results in the field of time-discrete control systems. Furthermore the numerical results give new insights into a Joint-Implementation Program and herewith, they may improve this important economic tool. The iterative solution presented at the end might be a useful orientation to anticipate and support Kyoto Process.

  10. Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mahmoudi, Babak; Pohlmeyer, Eric A.; Prins, Noeline W.; Geng, Shijia; Sanchez, Justin C.

    2013-12-01

    Objective. Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Approach. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. Main results. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. Significance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

  11. Short-Term Memory, Executive Control, and Children's Route Learning

    ERIC Educational Resources Information Center

    Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

    2012-01-01

    The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

  12. Impact of Cooperative Learning on Naval Air Traffic Controller Training.

    ERIC Educational Resources Information Center

    Holubec, Edythe; And Others

    1993-01-01

    Reports on a study of the impact of cooperative learning techniques, compared with traditional Navy instructional methods, on Navy air traffic controller trainees. Finds that cooperative learning methods improved higher level reasoning skills and resulted in no failures among the trainees. (CFR)

  13. Learned Helplessness: A Theory for the Age of Personal Control.

    ERIC Educational Resources Information Center

    Peterson, Christopher; And Others

    Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

  14. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  15. ITER safety challenges and opportunities

    SciTech Connect

    Piet, S.J.

    1991-01-01

    Results of the Conceptual Design Activity (CDA) for the International Thermonuclear Experimental Reactor (ITER) suggest challenges and opportunities. ITER is capable of meeting anticipated regulatory dose limits,'' but proof is difficult because of large radioactive inventories needing stringent radioactivity confinement. We need much research and development (R D) and design analysis to establish that ITER meets regulatory requirements. We have a further opportunity to do more to prove more of fusion's potential safety and environmental advantages and maximize the amount of ITER technology on the path toward fusion power plants. To fulfill these tasks, we need to overcome three programmatic challenges and three technical challenges. The first programmatic challenge is to fund a comprehensive safety and environmental ITER R D plan. Second is to strengthen safety and environment work and personnel in the international team. Third is to establish an external consultant group to advise the ITER Joint Team on designing ITER to meet safety requirements for siting by any of the Parties. The first of the three key technical challenges is plasma engineering -- burn control, plasma shutdown, disruptions, tritium burn fraction, and steady state operation. The second is the divertor, including tritium inventory, activation hazards, chemical reactions, and coolant disturbances. The third technical challenge is optimization of design requirements considering safety risk, technical risk, and cost. Some design requirements are now too strict; some are too lax. Fuel cycle design requirements are presently too strict, mandating inappropriate T separation from H and D. Heat sink requirements are presently too lax; they should be strengthened to ensure that maximum loss of coolant accident temperatures drop.

  16. Learning, attentional control and action video games

    PubMed Central

    Green, C.S.; Bavelier, D.

    2012-01-01

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games’ produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. PMID:22440805

  17. Learning, attentional control, and action video games.

    PubMed

    Green, C S; Bavelier, D

    2012-03-20

    While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.

  18. The real mission of ITER

    SciTech Connect

    Wurden, G A

    2009-01-01

    For future machines, the plasma stored energy is going up by factors of 20-40x, and plasma currents by 2-3x, while the surface to volume ratio is at the same time decreasing. Therefore the disruption forces, even for constant B, (which scale like IxB), and associated possible localized heating on machine components, are more severe. Notably, Tore Supra has demonstrated removal of more than 1 GJ of input energy, over nearly a 400 second period. However, the instantaneous stored energy in the Tore Supra system (which is most directly related to the potential for disruption damage) is quite small compared to other large tokamaks. The goal of ITER is routinely described as studying DT burning plasmas with a Q {approx} 10. In reality, ITER has a much more important first order mission. In fact, if it fails at this mission, the consequences are that ITER will never get to the eventual stated purpose of studying a burning plasma. The real mission of ITER is to study (and demonstrate successfully) plasma control with {approx}10-17 MA toroidal currents and {approx}100-400 MJ plasma stored energy levels in long-pulse scenarios. Before DT operation is ever given a go-ahead in ITER, the reality is that ITER must demonstrate routine and reliable control of high energy hydrogen (and deuterium) plasmas. The difficulty is that ITER must simultaneously deal with several technical problems: (1) heat removal at the plasma/wall interface, (2) protection of the wall components from off-normal events, and (3) generation of dust/redeposition of first wall materials. All previous tokamaks have encountered hundred's of major disruptions in the course of their operation. The consequences of a few MA of runaway electrons (at 20-50 MeV) being generated in ITER, and then being lost to the walls are simply catastrophic. They will not be deposited globally, but will drift out (up, down, whatever, depending on control system), and impact internal structures, unless 'ameliorated'. Basically, this

  19. Motor Skill Learning, Retention, and Control Deficits in Parkinson's Disease

    PubMed Central

    Pendt, Lisa Katharina; Reuter, Iris; Müller, Hermann

    2011-01-01

    Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance. PMID:21760898

  20. E-learning: controlling costs and increasing value.

    PubMed

    Walsh, Kieran

    2015-04-01

    E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods.

  1. Self-controlled practice benefits motor learning in older adults.

    PubMed

    Lessa, Helena Thofehrn; Chiviacowsky, Suzete

    2015-04-01

    Providing learners with the chance to choose over certain aspects of practice has been consistently shown to facilitate the acquisition of motor skills in several populations. However, studies investigating the effects of providing autonomy support during the learning process of older adults remain scarce. The objective of the present study was to investigate the effects of self-controlled amount of practice on the learning of a sequential motor task in older adults. Participants in the self-control group were able to choose when to stop practicing a speed cup stacking task, while the number of practice trials for a yoked group was pre-determined, mirroring the self-control group. The opportunity to choose when stop practicing facilitated motor performance and learning compared to the yoked condition. The findings suggest that letting older adult learners choose the amount of practice, supporting their autonomy needs, has a positive influence on motor learning.

  2. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  3. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  4. Online learning and control of attraction basins for the development of sensorimotor control strategies.

    PubMed

    de Rengervé, Antoine; Andry, Pierre; Gaussier, Philippe

    2015-04-01

    Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original PerAc model, path-following or place-reaching behaviors correspond to the sensorimotor attractors resulting from the dynamics of learned sensorimotor associations. The DM-PerAc model, inspired by human muscles, permits one to combine impedance-like control with the capability of learning sensorimotor attraction basins. We detail a solution to learn incrementally online the DM-PerAc visuomotor controller. Postural attractors are learned by adapting the muscle activations in the model depending on movement errors. Visuomotor categories merging visual and proprioceptive signals are associated with these muscle activations. Thus, the visual and proprioceptive signals activate the motor action generating an attractor which satisfies both visual and proprioceptive constraints. This visuomotor controller can serve as a basis for imitative behaviors. In addition, the muscle activation patterns can define directions of movement instead of postural attractors. Such patterns can be used in state-action couples to generate trajectories like in the PerAc model. We discuss a possible extension of the DM-PerAc controller by adapting the Fukuyori's controller based on the Langevin's equation. This controller can serve not only to reach attractors which were not explicitly learned, but also to learn the state/action couples to define trajectories.

  5. ECRH System For ITER

    SciTech Connect

    Darbos, C.; Henderson, M.; Gandini, F.; Albajar, F.; Bomcelli, T.; Heidinger, R.; Saibene, G.; Chavan, R.; Goodman, T.; Hogge, J. P.; Sauter, O.; Denisov, G.; Farina, D.; Kajiwara, K.; Kasugai, A.; Kobayashi, N.; Oda, Y.; Ramponi, G.

    2009-11-26

    A 26 MW Electron Cyclotron Heating and Current Drive (EC H and CD) system is to be installed for ITER. The main objectives are to provide, start-up assist, central H and CD and control of MHD activity. These are achieved by a combination of two types of launchers, one located in an equatorial port and the second type in four upper ports. The physics applications are partitioned between the two launchers, based on the deposition location and driven current profiles. The equatorial launcher (EL) will access from the plasma axis to mid radius with a relatively broad profile useful for central heating and current drive applications, while the upper launchers (ULs) will access roughly the outer half of the plasma radius with a very narrow peaked profile for the control of the Neoclassical Tearing Modes (NTM) and sawtooth oscillations. The EC power can be switched between launchers on a time scale as needed by the immediate physics requirements. A revision of all injection angles of all launchers is under consideration for increased EC physics capabilities while relaxing the engineering constraints of both the EL and ULs. A series of design reviews are being planned with the five parties (EU, IN, JA, RF, US) procuring the EC system, the EC community and ITER Organization (IO). The review meetings qualify the design and provide an environment for enhancing performances while reducing costs, simplifying interfaces, predicting technology upgrades and commercial availability. In parallel, the test programs for critical components are being supported by IO and performed by the Domestic Agencies (DAs) for minimizing risks. The wide participation of the DAs provides a broad representation from the EC community, with the aim of collecting all expertise in guiding the EC system optimization. Still a strong relationship between IO and the DA is essential for optimizing the design of the EC system and for the installation and commissioning of all ex-vessel components when several

  6. Performances of the fractal iterative method with an internal model control law on the ESO end-to-end ELT adaptive optics simulator

    NASA Astrophysics Data System (ADS)

    Béchet, C.; Le Louarn, M.; Tallon, M.; Thiébaut, É.

    2008-07-01

    Adaptive Optics systems under study for the Extremely Large Telescopes gave rise to a new generation of algorithms for both wavefront reconstruction and the control law. In the first place, the large number of controlled actuators impose the use of computationally efficient methods. Secondly, the performance criterion is no longer solely based on nulling residual measurements. Priors on turbulence must be inserted. In order to satisfy these two requirements, we suggested to associate the Fractal Iterative Method for the estimation step with an Internal Model Control. This combination has now been tested on an end-to-end adaptive optics numerical simulator at ESO, named Octopus. Results are presented here and performance of our method is compared to the classical Matrix-Vector Multiplication combined with a pure integrator. In the light of a theoretical analysis of our control algorithm, we investigate the influence of several errors contributions on our simulations. The reconstruction error varies with the signal-to-noise ratio but is limited by the use of priors. The ratio between the system loop delay and the wavefront coherence time also impacts on the reachable Strehl ratio. Whereas no instabilities are observed, correction quality is obviously affected at low flux, when subapertures extinctions are frequent. Last but not least, the simulations have demonstrated the robustness of the method with respect to sensor modeling errors and actuators misalignments.

  7. Facts and fiction of learning systems. [decision making intelligent control

    NASA Technical Reports Server (NTRS)

    Saridis, G. N.

    1975-01-01

    The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

  8. Novel reinforcement learning approach for difficult control problems

    NASA Astrophysics Data System (ADS)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  9. Energetic ions in ITER plasmas

    NASA Astrophysics Data System (ADS)

    Pinches, S. D.; Chapman, I. T.; Lauber, Ph. W.; Oliver, H. J. C.; Sharapov, S. E.; Shinohara, K.; Tani, K.

    2015-02-01

    This paper discusses the behaviour and consequences of the expected populations of energetic ions in ITER plasmas. It begins with a careful analytic and numerical consideration of the stability of Alfvén Eigenmodes in the ITER 15 MA baseline scenario. The stability threshold is determined by balancing the energetic ion drive against the dominant damping mechanisms and it is found that only in the outer half of the plasma ( r / a > 0.5 ) can the fast ions overcome the thermal ion Landau damping. This is in spite of the reduced numbers of alpha-particles and beam ions in this region but means that any Alfvén Eigenmode-induced redistribution is not expected to influence the fusion burn process. The influence of energetic ions upon the main global MHD phenomena expected in ITER's primary operating scenarios, including sawteeth, neoclassical tearing modes and Resistive Wall Modes, is also reviewed. Fast ion losses due to the non-axisymmetric fields arising from the finite number of toroidal field coils, the inclusion of ferromagnetic inserts, the presence of test blanket modules containing ferromagnetic material, and the fields created by the Edge Localised Mode (ELM) control coils in ITER are discussed. The greatest losses and associated heat loads onto the plasma facing components arise due to the use of the ELM control coils and come from neutral beam ions that are ionised in the plasma edge.

  10. ITER test programme

    NASA Astrophysics Data System (ADS)

    Abdou, M.; Baker, C.; Casini, G.

    1991-07-01

    The International Thermonuclear Experimental Reactor (ITER) was designed to operate in two phases. The first phase, which lasts for 6 years, is devoted to machine checkout and physics testing. The second phase lasts for 8 years and is devoted primarily to technology testing. This report describes the technology test program development for ITER, the ancillary equipment outside the torus necessary to support the test modules, the international collaboration aspects of conducting the test program on ITER, the requirements on the machine major parameters and the R and D program required to develop the test modules for testing in ITER.

  11. Self-Programmed Control: A New Approach to Learning.

    ERIC Educational Resources Information Center

    Barrios, Alfred A.

    This paper introduces a program for dealing with two affective detractors from learning: (1) a negative attitude toward school, and (2) personal problems. The program consists of three basic interacting components: (1) Self Programmed Control Technique Series--a method giving persons greater control over involuntary personality traits; (2)…

  12. Iterative control approach to high-speed force-distance curve measurement using AFM: time-dependent response of PDMS example.

    PubMed

    Kim, Kyong-Soo; Lin, Zhiqun; Shrotriya, Pranav; Sundararajan, Sriram; Zou, Qingze

    2008-08-01

    Force-distance curve measurements using atomic force microscope (AFM) has been widely used in a broad range of areas. However, currently force-curve measurements are hampered the its low speed of AFM. In this article, a novel inversion-based iterative control technique is proposed to dramatically increase the speed of force-curve measurements. Experimental results are presented to show that by using the proposed control technique, the speed of force-curve measurements can be increased by over 80 times--with no loss of spatial resolution--on a commercial AFM platform and with a standard cantilever. High-speed force curve measurements using this control technique are utilized to quantitatively study the time-dependent elastic modulus of poly(dimethylsiloxane) (PDMS). The force-curves employ a broad spectrum of push-in (load) rates, spanning two-order differences. The elastic modulus measured at low-speed compares well with the value obtained from dynamic mechanical analysis (DMA) test, and the value of the elastic modulus increases as the push-in rate increases, signifying that a faster external deformation rate transitions the viscoelastic response of PDMS from that of a rubbery material toward a glassy one. PMID:18467033

  13. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    NASA Astrophysics Data System (ADS)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  14. On the use of iterative techniques for feedforward control of transverse angle and position jitter in linear particle beam accelerators

    SciTech Connect

    Barr, D.S.

    1994-11-01

    It is possible to use feedforward predictive control for transverse position and trajectory-angle jitter correction. The control procedure is straightforward, but creation of the predictive filter is not as obvious. The two processes tested were the least mean squares (LMS) and Kalman inter methods. The controller parameters calculated offline are downloaded to a real-time analog correction system between macropulses. These techniques worked well for both interpulse (pulse-to-pulse) correction and intrapulse (within a pulse) correction with the Kalman filter method being the clear winner. A simulation based on interpulse data taken at the Stanford Linear Collider showed an improvement factor of almost three in the average rms jitter over standard feedback techniques for the Kalman filter. An improvement factor of over three was found for the Kalman filter on intrapulse data taken at the Los Alamos Meson Physics Facility. The feedforward systems also improved the correction bandwidth.

  15. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  16. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  17. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    PubMed Central

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  18. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  19. Recent developments in learning control and system identification for robots and structures

    NASA Technical Reports Server (NTRS)

    Phan, M.; Juang, J.-N.; Longman, R. W.

    1990-01-01

    This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

  20. Self-teaching neural network learns difficult reactor control problem

    SciTech Connect

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

  1. Can we (control) Engineer the degree learning process?

    NASA Astrophysics Data System (ADS)

    White, A. S.; Censlive, M.; Neilsen, D.

    2014-07-01

    This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

  2. Iteration, Not Induction

    ERIC Educational Resources Information Center

    Dobbs, David E.

    2009-01-01

    The main purpose of this note is to present and justify proof via iteration as an intuitive, creative and empowering method that is often available and preferable as an alternative to proofs via either mathematical induction or the well-ordering principle. The method of iteration depends only on the fact that any strictly decreasing sequence of…

  3. Learning control for batch thermal sterilization of canned foods.

    PubMed

    Syafiie, S; Tadeo, F; Villafin, M; Alonso, A A

    2011-01-01

    A control technique based on Reinforcement Learning is proposed for the thermal sterilization of canned foods. The proposed controller has the objective of ensuring a given degree of sterilization during Heating (by providing a minimum temperature inside the cans during a given time) and then a smooth Cooling, avoiding sudden pressure variations. For this, three automatic control valves are manipulated by the controller: a valve that regulates the admission of steam during Heating, and a valve that regulate the admission of air, together with a bleeder valve, during Cooling. As dynamical models of this kind of processes are too complex and involve many uncertainties, controllers based on learning are proposed. Thus, based on the control objectives and the constraints on input and output variables, the proposed controllers learn the most adequate control actions by looking up a certain matrix that contains the state-action mapping, starting from a preselected state-action space. This state-action matrix is constantly updated based on the performance obtained with the applied control actions. Experimental results at laboratory scale show the advantages of the proposed technique for this kind of processes.

  4. On the use of iterative techniques for feedforward control of transverse angle and position jitter in linear particle beam accelerators

    SciTech Connect

    Barr, D.S.

    1995-05-05

    It is possible to use feedforward predictive control for transverse position and trajectory-angle jitter correction. The control procedure is straightforward, but creation of the predictive filter is not as obvious. The two process tested were the least mean squares (LMS) and Kalman filter methods. The controller parameters calculated offline are downloaded to a real-time analog correction system between macropulses. These techniques worked well for both interpulse (pulse-to-pulse) correction and intrapulse (within a pulse) correction with the Kalman filter method being the clear winner. A simulation based on interpulse data taken at the Stanford Linear Collider showed an improvement factor of almost three in the average rms jitter over standard feedback techniques for the Kalman filter. An improvement factor of over three was found for the Kalman filter on intrapulse data taken at the Los Alamos Meson Physics Facility. The feedforward systems also improved the correction bandwidth. {copyright} {ital 1995} {ital American} {ital Institute} {ital of} {ital Physics}.

  5. Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Merrill, Walter C.

    1990-01-01

    The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

  6. Sampling-based learning control of inhomogeneous quantum ensembles

    NASA Astrophysics Data System (ADS)

    Chen, Chunlin; Dong, Daoyi; Long, Ruixing; Petersen, Ian R.; Rabitz, Herschel A.

    2014-02-01

    Compensation for parameter dispersion is a significant challenge for control of inhomogeneous quantum ensembles. In this paper, we present the systematic methodology of sampling-based learning control (SLC) for simultaneously steering the members of inhomogeneous quantum ensembles to the same desired state. The SLC method is employed for optimal control of the state-to-state transition probability for inhomogeneous quantum ensembles of spins as well as Λ-type atomic systems. The procedure involves the steps of (i) training and (ii) testing. In the training step, a generalized system is constructed by sampling members according to the distribution of inhomogeneous parameters drawn from the ensemble. A gradient flow based learning and optimization algorithm is adopted to find an optimal control for the generalized system. In the process of testing, a number of additional ensemble members are randomly selected to evaluate the control performance. Numerical results are presented, showing the effectiveness of the SLC method.

  7. Learning Switching Control: A Tank Level-Control Exercise

    ERIC Educational Resources Information Center

    Pasamontes, M.; Alvarez, J. D.; Guzman, J. L.; Berenguel, M.

    2012-01-01

    A key topic in multicontroller strategies is the mechanism for switching between controllers, depending on the current operating point. The objective of the switching mechanism is to keep the control action coherent. To help students understand the switching strategy involved in multicontroller schema and the relationship between the system…

  8. Cortical control: learning from the lamprey.

    PubMed

    Lopes, Gonçalo; Kampff, Adam R

    2015-03-01

    The function of the motor cortex has been a persistent mystery. A recent study has found striking correspondence between the descending projections of lamprey pallium and mammalian motor cortex, encouraging comparative studies of the origin (and role) of forebrain motor control.

  9. On-line controlled documents: Lessons learned

    SciTech Connect

    Cochrell, R.C.; Steele, C.M.

    1995-06-01

    Placing Controlled Documents on-line on a computer network seems like the solution to many problems, one being distribution, with a path toward a paperless office. However, many problems presented themselves as we were designing the system and placing the documents on-line. Although we planned and established a Process Management Team to help work out the bugs, we still encountered many obstacles in the process. This presentation will cover the ``trials and tribulations`` of placing Controlled Documents on a computer network at three different sites. We will discuss the process we went through, the problems we encountered, the software we used, and how we got management to buy into the process.

  10. Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control.

    PubMed

    von Stülpnagel, Rul; Steffens, Melanie C

    2013-09-01

    Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement.

  11. Learned helplessness, depression, and the illusion of control.

    PubMed

    Alloy, L B; Abramson, L Y

    1982-06-01

    Do people previously exposed to uncontrollable aversive events, like naturally depressed people, fail to succumb to an illusion of control in a situation in which events occur noncontingently but are associated with success? Depressed and nondepressed college students were assigned to one of three groups that make up the typical triad used in studies of learned helplessness: controllable noises, uncontrollable noises, or no noises. Following pretreatment, subjects judged how much control they had in a noncontingency learning problem. For half of the subjects, events were noncontingent and associated with failure; whereas for the remaining subjects, events were noncontingent but associated with success. Contrary to the predictions of learned helplessness theory, nondepressed subjects previously exposed to uncontrollable noises showed a robust illusion of control in the condition in which events were noncontingent but associated with success, whereas nondepressed subjects previously exposed to controllable noises judged control accurately. Depressed subjects also judged control accurately regardless of their previous noise experience, The results were interpreted as consistent with the egotism hypothesis.

  12. Patients with Parkinson's Disease Learn to Control Complex Systems via Procedural as Well as Non-Procedural Learning

    ERIC Educational Resources Information Center

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that…

  13. Near-complete elimination of mutant mtDNA by iterative or dynamic dose-controlled treatment with mtZFNs.

    PubMed

    Gammage, Payam A; Gaude, Edoardo; Van Haute, Lindsey; Rebelo-Guiomar, Pedro; Jackson, Christopher B; Rorbach, Joanna; Pekalski, Marcin L; Robinson, Alan J; Charpentier, Marine; Concordet, Jean-Paul; Frezza, Christian; Minczuk, Michal

    2016-09-19

    Mitochondrial diseases are frequently associated with mutations in mitochondrial DNA (mtDNA). In most cases, mutant and wild-type mtDNAs coexist, resulting in heteroplasmy. The selective elimination of mutant mtDNA, and consequent enrichment of wild-type mtDNA, can rescue pathological phenotypes in heteroplasmic cells. Use of the mitochondrially targeted zinc finger-nuclease (mtZFN) results in degradation of mutant mtDNA through site-specific DNA cleavage. Here, we describe a substantial enhancement of our previous mtZFN-based approaches to targeting mtDNA, allowing near-complete directional shifts of mtDNA heteroplasmy, either by iterative treatment or through finely controlled expression of mtZFN, which limits off-target catalysis and undesired mtDNA copy number depletion. To demonstrate the utility of this improved approach, we generated an isogenic distribution of heteroplasmic cells with variable mtDNA mutant level from the same parental source without clonal selection. Analysis of these populations demonstrated an altered metabolic signature in cells harbouring decreased levels of mutant m.8993T>G mtDNA, associated with neuropathy, ataxia, and retinitis pigmentosa (NARP). We conclude that mtZFN-based approaches offer means for mtDNA heteroplasmy manipulation in basic research, and may provide a strategy for therapeutic intervention in selected mitochondrial diseases.

  14. Near-complete elimination of mutant mtDNA by iterative or dynamic dose-controlled treatment with mtZFNs.

    PubMed

    Gammage, Payam A; Gaude, Edoardo; Van Haute, Lindsey; Rebelo-Guiomar, Pedro; Jackson, Christopher B; Rorbach, Joanna; Pekalski, Marcin L; Robinson, Alan J; Charpentier, Marine; Concordet, Jean-Paul; Frezza, Christian; Minczuk, Michal

    2016-09-19

    Mitochondrial diseases are frequently associated with mutations in mitochondrial DNA (mtDNA). In most cases, mutant and wild-type mtDNAs coexist, resulting in heteroplasmy. The selective elimination of mutant mtDNA, and consequent enrichment of wild-type mtDNA, can rescue pathological phenotypes in heteroplasmic cells. Use of the mitochondrially targeted zinc finger-nuclease (mtZFN) results in degradation of mutant mtDNA through site-specific DNA cleavage. Here, we describe a substantial enhancement of our previous mtZFN-based approaches to targeting mtDNA, allowing near-complete directional shifts of mtDNA heteroplasmy, either by iterative treatment or through finely controlled expression of mtZFN, which limits off-target catalysis and undesired mtDNA copy number depletion. To demonstrate the utility of this improved approach, we generated an isogenic distribution of heteroplasmic cells with variable mtDNA mutant level from the same parental source without clonal selection. Analysis of these populations demonstrated an altered metabolic signature in cells harbouring decreased levels of mutant m.8993T>G mtDNA, associated with neuropathy, ataxia, and retinitis pigmentosa (NARP). We conclude that mtZFN-based approaches offer means for mtDNA heteroplasmy manipulation in basic research, and may provide a strategy for therapeutic intervention in selected mitochondrial diseases. PMID:27466392

  15. Near-complete elimination of mutant mtDNA by iterative or dynamic dose-controlled treatment with mtZFNs

    PubMed Central

    Gammage, Payam A.; Gaude, Edoardo; Van Haute, Lindsey; Rebelo-Guiomar, Pedro; Jackson, Christopher B.; Rorbach, Joanna; Pekalski, Marcin L.; Robinson, Alan J.; Charpentier, Marine; Concordet, Jean-Paul; Frezza, Christian; Minczuk, Michal

    2016-01-01

    Mitochondrial diseases are frequently associated with mutations in mitochondrial DNA (mtDNA). In most cases, mutant and wild-type mtDNAs coexist, resulting in heteroplasmy. The selective elimination of mutant mtDNA, and consequent enrichment of wild-type mtDNA, can rescue pathological phenotypes in heteroplasmic cells. Use of the mitochondrially targeted zinc finger-nuclease (mtZFN) results in degradation of mutant mtDNA through site-specific DNA cleavage. Here, we describe a substantial enhancement of our previous mtZFN-based approaches to targeting mtDNA, allowing near-complete directional shifts of mtDNA heteroplasmy, either by iterative treatment or through finely controlled expression of mtZFN, which limits off-target catalysis and undesired mtDNA copy number depletion. To demonstrate the utility of this improved approach, we generated an isogenic distribution of heteroplasmic cells with variable mtDNA mutant level from the same parental source without clonal selection. Analysis of these populations demonstrated an altered metabolic signature in cells harbouring decreased levels of mutant m.8993T>G mtDNA, associated with neuropathy, ataxia, and retinitis pigmentosa (NARP). We conclude that mtZFN-based approaches offer means for mtDNA heteroplasmy manipulation in basic research, and may provide a strategy for therapeutic intervention in selected mitochondrial diseases. PMID:27466392

  16. Language Learning and Control in Monolinguals and Bilinguals

    ERIC Educational Resources Information Center

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals…

  17. Learned control of slow potential interhemispheric asymmetry in schizophrenia.

    PubMed

    Gruzelier, J; Hardman, E; Wild, J; Zaman, R

    1999-12-01

    We report on the feasibility of teaching 16 (DSM-IV) schizophrenic patients, subdivided by syndrome, self-regulation of interhemispheric asymmetry having demonstrated efficient learning of interhemispheric control in normal subjects. Reversal of asymmetry may be important to treatment and recovery in schizophrenia for following improvement on neuroleptic drugs functional hemispheric asymmetries have reversed, with directions of reversal and pre-existing asymmetry dependent on syndrome. Asymmetry reversal in animals, manifested by spatial turning tendencies, has been used as a marker of neuroleptic action and involves striatal dopamine under reciprocal hemispheric control. We gave as feedback the left right asymmetry in slow potential negativity recorded from the sensory motor strip (C3,4). Feedback took the form of a rocket on a screen which rose or fell with leftward or rightward shifts in negativity. Patients were able to learn control (P < 0.01). In those patients with lesser ability this was due to inability to sustain concentration throughout the session rather than slow initial learning. Active syndrome patients were better able to shift negativity rightward and withdrawn patients leftward, directions associated with drug reversal of functional asymmetry and symptom recovery for each syndrome. Accordingly our demonstration that many symptomatic schizophrenic patients are capable of learning control opens the door to electrocortical operant conditioning training in schizophrenia with therapeutic regimens.

  18. Adolescents' Communication Styles and Learning about Birth Control.

    ERIC Educational Resources Information Center

    De Pietro, Rocco; Allen, Richard L.

    1984-01-01

    Identified predictors of birth control knowledge resulting from interactant or noninteractant communication styles in 100 adolescents who read a magazine on human sexuality. Data suggested that the interactant style was most beneficial for new learning. Gender and the presence of siblings in the home were important moderators. (JAC)

  19. Multi Car Elevator Control by using Learning Automaton

    NASA Astrophysics Data System (ADS)

    Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

    We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

  20. Attention control learning in the decision space using state estimation

    NASA Astrophysics Data System (ADS)

    Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

    2016-05-01

    The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

  1. Perl Modules for Constructing Iterators

    NASA Technical Reports Server (NTRS)

    Tilmes, Curt

    2009-01-01

    The Iterator Perl Module provides a general-purpose framework for constructing iterator objects within Perl, and a standard API for interacting with those objects. Iterators are an object-oriented design pattern where a description of a series of values is used in a constructor. Subsequent queries can request values in that series. These Perl modules build on the standard Iterator framework and provide iterators for some other types of values. Iterator::DateTime constructs iterators from DateTime objects or Date::Parse descriptions and ICal/RFC 2445 style re-currence descriptions. It supports a variety of input parameters, including a start to the sequence, an end to the sequence, an Ical/RFC 2445 recurrence describing the frequency of the values in the series, and a format description that can refine the presentation manner of the DateTime. Iterator::String constructs iterators from string representations. This module is useful in contexts where the API consists of supplying a string and getting back an iterator where the specific iteration desired is opaque to the caller. It is of particular value to the Iterator::Hash module which provides nested iterations. Iterator::Hash constructs iterators from Perl hashes that can include multiple iterators. The constructed iterators will return all the permutations of the iterations of the hash by nested iteration of embedded iterators. A hash simply includes a set of keys mapped to values. It is a very common data structure used throughout Perl programming. The Iterator:: Hash module allows a hash to include strings defining iterators (parsed and dispatched with Iterator::String) that are used to construct an overall series of hash values.

  2. Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

  3. Human-level control through deep reinforcement learning.

    PubMed

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks

  4. Human-level control through deep reinforcement learning

    NASA Astrophysics Data System (ADS)

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-01

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  5. Human-level control through deep reinforcement learning.

    PubMed

    Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

    2015-02-26

    The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

  6. [Lessons learned from tobacco control in Spain].

    PubMed

    Fernández, Esteve; Villalbí, Joan R; Córdoba, Rodrigo

    2006-01-01

    The growing involvement in Spain by civil society in the demand for tobacco control policies has been notable. The basis for the creation of the National Committee for Tobacco Prevention was established in 2004. At the end of that year, an intensive intervention was aimed at specifying, in law, the regulatory actions in the National Plan for Tobacco Prevention. This would facilitate a qualitative leap, taking advantage of the legal transposition of the European directive on advertising. With broad political consensus, the Law 28/2005 was established regarding sanitary measures for tobacco and the regulation of the sale, supply and consumption of tobacco products. The objective stated in this law is to prevent the initiation of tobacco consumption, especially among youth, guarantee the right of non-smokers to breathe air free from tobacco smoke and make quitting this habit easier for people who wish to do so. The main issues included are the prohibition of tobacco advertising and the limitation of tobacco consumption in common work areas and enclosed public spaces. The new law has replaced the previous rules in Spain, which were some of the most permissive in the European Union in terms of tobacco sales, advertising limitations and restrictions on smoking locations. It is clear that there is still much to be done. At this time, more social support needs to be generated in favor of the new regulations, and an important effort needs to be made to educate the public.

  7. Decentralized reinforcement-learning control and emergence of motion patterns

    NASA Astrophysics Data System (ADS)

    Svinin, Mikhail; Yamada, Kazuyaki; Okhura, Kazuhiro; Ueda, Kanji

    1998-10-01

    In this paper we propose a system for studying emergence of motion patterns in autonomous mobile robotic systems. The system implements an instance-based reinforcement learning control. Three spaces are of importance in formulation of the control scheme. They are the work space, the sensor space, and the action space. Important feature of our system is that all these spaces are assumed to be continuous. The core part of the system is a classifier system. Based on the sensory state space analysis, the control is decentralized and is specified at the lowest level of the control system. However, the local controllers are implicitly connected through the perceived environment information. Therefore, they constitute a dynamic environment with respect to each other. The proposed control scheme is tested under simulation for a mobile robot in a navigation task. It is shown that some patterns of global behavior--such as collision avoidance, wall-following, light-seeking--can emerge from the local controllers.

  8. Mode conversion in ITER

    NASA Astrophysics Data System (ADS)

    Jaeger, E. F.; Berry, L. A.; Myra, J. R.

    2006-10-01

    Fast magnetosonic waves in the ion cyclotron range of frequencies (ICRF) can convert to much shorter wavelength modes such as ion Bernstein waves (IBW) and ion cyclotron waves (ICW) [1]. These modes are potentially useful for plasma control through the generation of localized currents and sheared flows. As part of the SciDAC Center for Simulation of Wave-Plasma Interactions project, the AORSA global-wave solver [2] has been ported to the new, dual-core Cray XT-3 (Jaguar) at ORNL where it demonstrates excellent scaling with the number of processors. Preliminary calculations using 4096 processors have allowed the first full-wave simulations of mode conversion in ITER. Mode conversion from the fast wave to the ICW is observed in mixtures of deuterium, tritium and helium3 at 53 MHz. The resulting flow velocity and electric field shear will be calculated. [1] F.W. Perkins, Nucl. Fusion 17, 1197 (1977). [2] E.F. Jaeger, L.A. Berry, J.R. Myra, et al., Phys. Rev. Lett. 90, 195001-1 (2003).

  9. Diagnostics for ITER

    SciTech Connect

    Donne, A. J. H.; Hellermann, M. G. von; Barnsley, R.

    2008-10-22

    After an introduction into the specific challenges in the field of diagnostics for ITER (specifically high level of nuclear radiation, long pulses, high fluxes of particles to plasma facing components, need for reliability and robustness), an overview will be given of the spectroscopic diagnostics foreseen for ITER. The paper will describe both active neutral-beam based diagnostics as well as passive spectroscopic diagnostics operating in the visible, ultra-violet and x-ray spectral regions.

  10. ITER convertible blanket evaluation

    SciTech Connect

    Wong, C.P.C.; Cheng, E.

    1995-09-01

    Proposed International Thermonuclear Experimental Reactor (ITER) convertible blankets were reviewed. Key design difficulties were identified. A new particle filter concept is introduced and key performance parameters estimated. Results show that this particle filter concept can satisfy all of the convertible blanket design requirements except the generic issue of Be blanket lifetime. If the convertible blanket is an acceptable approach for ITER operation, this particle filter option should be a strong candidate.

  11. Toward Construction of ITER

    NASA Astrophysics Data System (ADS)

    Shimomura, Yasuo

    The ITER Project has been significantly developed in the past years in preparation for its construction. The ITER Negotiators have developed a draft Joint Implementation Agreement (JIA), ready for completion following the nomination of the Project’s Director General (DG). The ITER International Team and Participant Teams have continued technical and organizational preparations. The actual construction will be able to start immediately after the international ITER organization will be established, following signature of the JIA. The Project is now strongly supported by all the participants as well as by the scientific community with the final high-level negotiations, focused on siting and the concluding details of cost sharing, started in December 2003. The EU, with Cadarache, and Japan, with Rokkasho, have both promised large contributions to the project to strongly support their construction site proposals. The extent to which they both wish to host the ITER facility is such that large contributions to a broader collaboration among the Parties are also proposed by them. This covers complementary activities to help accelerate fusion development towards a viable power source, as well as may allow the Participants to reach a conclusion on ITER siting.

  12. Learning and Control Model of the Arm for Loading

    NASA Astrophysics Data System (ADS)

    Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

    We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

  13. Reinforcement learning to adaptive control of nonlinear systems.

    PubMed

    Hwang, Kao-Shing; Tan, S W; Tsai, Min-Cheng

    2003-01-01

    Based on the feedback linearization theory, this paper presents how a reinforcement learning scheme that is adopted to construct artificial neural networks (ANNs) can linearize a nonlinear system effectively. The proposed reinforcement linearization learning system (RLLS) consists of two sub-systems: The evaluation predictor (EP) is a long-term policy selector, and the other is a short-term action selector composed of linearizing control (LC) and reinforce predictor (RP) elements. In addition, a reference model plays the role of the environment, which provides the reinforcement signal to the linearizing process. The RLLS thus receives reinforcement signals to accomplish the linearizing behavior to control a nonlinear system such that it can behave similarly to the reference model. Eventually, the RLLS performs identification and linearization concurrently. Simulation results demonstrate that the proposed learning scheme, which is applied to linearizing a pendulum system, provides better control reliability and robustness than conventional ANN schemes. Furthermore, a PI controller is used to control the linearized plant where the affine system behaves like a linear system.

  14. Design and Control of Large Collections of Learning Agents

    NASA Technical Reports Server (NTRS)

    Agogino, Adrian

    2001-01-01

    The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

  15. Neuromotor issues in the learning and control of golf skill.

    PubMed

    Knight, Christopher A

    2004-03-01

    Theoretical and practical issues related to the neuromotor control of a golf swing are presented in this paper. The typical strategy for golf training consists of high volume repetition with an emphasis on a large variety of isolated swing characteristics. The student is frequently instructed to maintain consistent performance in each swing with absolute invariance. Based on dynamical systems and motor control schema perspectives, it is argued that golfers can learn a more reliable swing by exploring swing parameters and focusing on higher order control principles that reduce the vast number of degrees of freedom. Some candidate training practices are proposed for applying these theoretical issues into practice. PMID:15532356

  16. The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

    ERIC Educational Resources Information Center

    Sorgenfrei, Christian; Smolnik, Stefan

    2016-01-01

    E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

  17. Automaticity and cognitive control in the learned predictiveness effect.

    PubMed

    Shone, Lauren T; Harris, Irina M; Livesey, Evan J

    2015-01-01

    In novel contexts, learning is biased toward cues previously experienced as predictive compared with cues previously experienced as nonpredictive. This is known as learned predictiveness. A recent finding has shown that instructions issued about the causal status of cues influences the expression of learned predictiveness, suggesting that controlled, volitional processes play a role in this effect. Three experiments are reported further investigating the effects of instructional manipulations on learned predictiveness. Experiment 1 confirms the influence of inferential processes, extending previous work to suggest that instructions affect associative memory as well as causal reasoning. Experiments 2 and 3 used a procedure designed to tease apart inferential and automatic contributions to the bias by presenting instructed causes that were previously predictive and previously nonpredictive. The results demonstrate that the prior predictiveness of cues influences subsequent learning over and above the effect of explicit instruction. However, it appears that the relationship between explicit instruction and predictive history is interactive rather than additive. Potential explanations for this interactivity are discussed.

  18. Effects of Locus of Control and Learner-Control on Web-Based Language Learning

    ERIC Educational Resources Information Center

    Chang, Mei-Mei; Ho, Chiung-Mei

    2009-01-01

    The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

  19. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    NASA Technical Reports Server (NTRS)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  20. Lanczos iterated time-reversal.

    PubMed

    Oberai, Assad A; Feijóo, Gonzalo R; Barbone, Paul E

    2009-02-01

    A new iterative time-reversal algorithm capable of identifying and focusing on multiple scatterers in a relatively small number of iterations is developed. It is recognized that the traditional iterated time-reversal method is based on utilizing power iterations to determine the dominant eigenpairs of the time-reversal operator. The convergence properties of these iterations are known to be suboptimal. Motivated by this, a new method based on Lanczos iterations is developed. In several illustrative examples it is demonstrated that for the same number of transmitted and received signals, the Lanczos iterations based approach is substantially more accurate. PMID:19206835

  1. Learning the Optimal Control of Coordinated Eye and Head Movements

    PubMed Central

    Saeb, Sohrab; Weber, Cornelius; Triesch, Jochen

    2011-01-01

    Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements. PMID:22072953

  2. Self-learning fuzzy controllers based on temporal back propagation

    NASA Technical Reports Server (NTRS)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  3. Neural network learning of optimal Kalman prediction and control.

    PubMed

    Linsker, Ralph

    2008-11-01

    Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw inferences for biological function. It has been suggested that the local cortical circuit (LCC) architecture may perform core functions (as yet unknown) that underlie sensory, motor, and other cortical processing. It is reasonable to conjecture that such functions may include prediction, the estimation or inference of missing or noisy sensory data, and the goal-driven generation of control signals. The resemblances found between the KPC NN architecture and that of the LCC are consistent with this conjecture.

  4. Reinforcement learning techniques for controlling resources in power networks

    NASA Astrophysics Data System (ADS)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  5. Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

    NASA Astrophysics Data System (ADS)

    Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

    2014-08-01

    This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

  6. E-Learning System for Learning Virtual Circuit Making with a Microcontroller and Programming to Control a Robot

    ERIC Educational Resources Information Center

    Takemura, Atsushi

    2015-01-01

    This paper proposes a novel e-Learning system for learning electronic circuit making and programming a microcontroller to control a robot. The proposed e-Learning system comprises a virtual-circuit-making function for the construction of circuits with a versatile, Arduino microcontroller and an educational system that can simulate behaviors of…

  7. Force and position control of robot manipulators: Learning and repetitive control approach

    NASA Astrophysics Data System (ADS)

    Jeon, Doyoung

    When a robot performs the same task repeatedly, a learning or repetitive controller can enhance the performance of the system significantly. Learning or repetitive control, however, has not been studied in the force control of a robot manipulator as extensively as in the position control of a robot manipulator. In this dissertation, learning control is applied to hybrid force and position control of robot manipulators. Also, repetitive control is applied to the control of a spring loaded end effector. When the geometry and position of the constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhances the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be stable in the sense of Lyapunov. In the experiments, a two degree of freedom SCARA-type direct-drive robot manipulator is used to test the feasibility of the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N (within the robot-mean-square force error of 1.95 N) and with desired tangential velocity. Considering the loss of contact observed at the initial trial, the performance of the system improved significantly. A spring loaded end effector is useful in assembly operations such as mounting an electronics package on a board. With the known stiffness of the spring, the desired contact force tracking is accomplished by controlling the spring displacement. Uncertainties in the environment such as the stiffness of the board make it difficult to program the required control force. As the operation repeats, the tracking errors for the spring displacement, i.e., the contact force, are

  8. Learning to push and learning to move: the adaptive control of contact forces

    PubMed Central

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

  9. Learning to push and learning to move: the adaptive control of contact forces.

    PubMed

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in "compatible pairs" connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions. PMID

  10. Optimal control in microgrid using multi-agent reinforcement learning.

    PubMed

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode.

  11. Optimal control in microgrid using multi-agent reinforcement learning.

    PubMed

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. PMID:22824135

  12. Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

    NASA Astrophysics Data System (ADS)

    Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

    2008-08-01

    In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

  13. Incoherent control of quantum systems with wavefunction-controllable subspaces via quantum reinforcement learning.

    PubMed

    Dong, Daoyi; Chen, Chunlin; Tarn, Tzyh-Jong; Pechen, Alexander; Rabitz, Herschel

    2008-08-01

    In this paper, an incoherent control scheme for accomplishing the state control of a class of quantum systems which have wavefunction-controllable subspaces is proposed. This scheme includes the following two steps: projective measurement on the initial state and learning control in the wavefunction-controllable subspace. The first step probabilistically projects the initial state into the wavefunction-controllable subspace. The probability of success is sensitive to the initial state; however, it can be greatly improved through multiple experiments on several identical initial states even in the case with a small probability of success for an individual measurement. The second step finds a local optimal control sequence via quantum reinforcement learning and drives the controlled system to the objective state through a set of suitable controls. In this strategy, the initial states can be unknown identical states, the quantum measurement is used as an effective control, and the controlled system is not necessarily unitarily controllable. This incoherent control scheme provides an alternative quantum engineering strategy for locally controllable quantum systems.

  14. Robust iterative methods

    SciTech Connect

    Saadd, Y.

    1994-12-31

    In spite of the tremendous progress achieved in recent years in the general area of iterative solution techniques, there are still a few obstacles to the acceptance of iterative methods in a number of applications. These applications give rise to very indefinite or highly ill-conditioned non Hermitian matrices. Trying to solve these systems with the simple-minded standard preconditioned Krylov subspace methods can be a frustrating experience. With the mathematical and physical models becoming more sophisticated, the typical linear systems which we encounter today are far more difficult to solve than those of just a few years ago. This trend is likely to accentuate. This workshop will discuss (1) these applications and the types of problems that they give rise to; and (2) recent progress in solving these problems with iterative methods. The workshop will end with a hopefully stimulating panel discussion with the speakers.

  15. Rescheduling with iterative repair

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Davis, Eugene; Daun, Brian; Deale, Michael

    1992-01-01

    This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing.

  16. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    SciTech Connect

    La Haye, R. J.

    2015-12-10

    ITER is an international project to design and build an experimental fusion reactor based on the “tokamak” concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of “H-mode” and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the “missing” current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM “seeding” instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a “wild card” may be broadening of the localized

  17. A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

    NASA Astrophysics Data System (ADS)

    La Haye, R. J.

    2015-12-01

    ITER is an international project to design and build an experimental fusion reactor based on the "tokamak" concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of "H-mode" and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the "missing" current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM "seeding" instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a "wild card" may be broadening of the localized ECCD by the presence of

  18. CONTROLLING ABSOLUTE FREQUENCY OF FEEDBACK IN A SELF-CONTROLLED SITUATION ENHANCES MOTOR LEARNING.

    PubMed

    Tsai, Min-Jen; Jwo, Hank

    2015-12-01

    The guidance hypothesis suggested that excessive extrinsic feedback facilitates motor performance but blocks the processing of intrinsic information. The present study tested the tenet of guidance hypothesis in self-controlled feedback by controlling the feedback frequency. The motor learning effect of limiting absolute feedback frequency was examined. Thirty-six participants (25 men, 11 women; M age=25.1 yr., SD=2.2) practiced a hand-grip force control task on a dynamometer by the non-dominant hand with varying amounts of feedback. They were randomly assigned to: (a) Self-controlled, (b) Yoked with self-controlled, and (c) Limited self-controlled conditions. In acquisition, two-way analysis of variance indicated significantly lower absolute error in both the yoked and limited self-controlled groups than the self-controlled group. The effect size of absolute error between trials with feedback and without feedback in the limited self-controlled condition was larger than that of the self-controlled condition. In the retention and transfer tests, the Limited self-controlled feedback group had significantly lower absolute error than the other two groups. The results indicated an increased motor learning effect of limiting absolute frequency of feedback in the self-controlled condition.

  19. An adaptive learning control system for large flexible structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.

    1985-01-01

    The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

  20. The role of heating and current drive in ITER

    SciTech Connect

    Nevins, W.M.; Haney, S.

    1993-10-18

    This report discusses and summarize the role of heating and non-inductive current drive in ITER as: (1) ITER must have heating power sufficient for ignition. (2) The heating system must be capable of current drive. (3) Steady-state operation is an ``ultimate goal.`` It is recognized that additional heating and current drive power (beyond what is initially installed on ITER) may be required. (4) The ``Ultimate goal of steady-state operation`` means steady-state with Q{sub CD} {ge} 5. Unlike the ``Terms of Reference`` for the ITER CDA, the ``ITER Technical Objectives and Approaches`` for the EDA sets no goal for the neutron wall load during steady-state operation. (5) In addition to bulk current drive, the ITER heating and current drive system should be used for current profile control and for burn control.

  1. Differences in learning between hyperprolinemic mice and their congenic controls.

    PubMed

    Davis, J L; Pico, R M; Flood, J F

    1987-07-01

    These experiments expanded earlier work on hyperprolinemic mice which showed learning deficits. The following behavioral tasks were used: step-through, passive avoidance; T-maze acquisition; shuttlebox acquisition, and radial-arm maze. Mouse species included PRO/Re-bb (genetically hyperprolinemic mice) and PRO/Re-aa (congenic nonhyperprolinemic controls) obtained from the Jackson Breeding Laboratories. Hyperprolinemic mice were impaired in acquiring T-maze and shuttlebox footshock avoidance behavior. One-trial passive avoidance behavior did not clearly differentiate between the groups. Radial maze performance was poor in both groups due possibly to observed acrophobia and lack of exploratory behavior. The results of this study combined with previously published work suggest that high-brain proline in conjunction with other amino acid changes account for the learning deficits.

  2. ITER Fusion Energy

    ScienceCinema

    Dr. Norbert Holtkamp

    2016-07-12

    ITER (in Latin “the way”) is designed to demonstrate the scientific and technological feasibility of fusion energy. Fusion is the process by which two light atomic nuclei combine to form a heavier over one and thus release energy. In the fusion process two isotopes of hydrogen – deuterium and tritium – fuse together to form a helium atom and a neutron. Thus fusion could provide large scale energy production without greenhouse effects; essentially limitless fuel would be available all over the world. The principal goals of ITER are to generate 500 megawatts of fusion power for periods of 300 to 500 seconds with a fusion power multiplication factor, Q, of at least 10. Q ? 10 (input power 50 MW / output power 500 MW). The ITER Organization was officially established in Cadarache, France, on 24 October 2007. The seven members engaged in the project – China, the European Union, India, Japan, Korea, Russia and the United States – represent more than half the world’s population. The costs for ITER are shared by the seven members. The cost for the construction will be approximately 5.5 billion Euros, a similar amount is foreseen for the twenty-year phase of operation and the subsequent decommissioning.

  3. An Iterative Angle Trisection

    ERIC Educational Resources Information Center

    Muench, Donald L.

    2007-01-01

    The problem of angle trisection continues to fascinate people even though it has long been known that it can't be done with straightedge and compass alone. However, for practical purposes, a good iterative procedure can get you as close as you want. In this note, we present such a procedure. Using only straightedge and compass, our procedure…

  4. Iterative software kernels

    SciTech Connect

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  5. ITER global stability limits

    SciTech Connect

    Hogan, J.T.; Uckan, N.A.

    1990-01-01

    The MHD stability limits to the ITER operational space have been examined with the PEST ideal stability code. Constraints on ITER operation have been examined for the nominal operational scenarios and for possible design variants. Rather than rely on evaluation of a relatively small number of sample cases, the approach has been to construct an approximation to the overall operational space, and to compare this with the observed limits in high-{beta} tokamaks. An extensive database with {approximately}20,000 stability results has been compiled for use by the ITER design team. Results from these studies show that the design values of the Troyon factor (g {approximately} 2.5 for ignition studies, and g {approximately} 3 for the technology phase) which are based on present experiments, are also expected to be attainable for ITER conditions, for which the configuration and wall-stabilisation environment differ from those in present experiments. Strongly peaked pressure profiles lead to degraded high-{beta} performance. Values of g {approximately} 4 are found for higher safety factor (q {sub {Psi}} {le} 4) than that of the present design (q{sub {Psi}} {approximately} 3). Profiles with q(0) < 1 are shown to give g {approximately} 2.5, if the current density profile provides optimum shear. The overall operational spaces are presented for g-q{sub {Psi}}, q{sub {Psi}}-1{sub i}, q-{alpha}{sub p} and l{sub i}-q{sub {psi}}.

  6. Machine learning of parameter control doctrine for sensor and communication systems. Final report

    SciTech Connect

    Kamen, R.B.; Dillard, R.A.

    1988-07-01

    Artificial-intelligence approaches to learning were reviewed for their potential contributions to the construction of a system to learn parameter-control doctrine. Separate learning tasks were isolated and several levels of related problems were distinguished. Formulas for providing the learning system with measures of its performance were derived for four kinds of targets.

  7. Effects of Dispositional Mindfulness on the Self-Controlled Learning of a Novel Motor Task

    ERIC Educational Resources Information Center

    Kee, Ying Hwa; Liu, Yeou-Teh

    2011-01-01

    Current literature suggests that mindful learning is beneficial to learning but its links with motor learning is seldom examined. In the present study, we examine the effects of learners' mindfulness disposition on the self-controlled learning of a novel motor task. Thirty-two participants undertook five practice sessions, in addition to a pre-,…

  8. Humans and Monkeys Exert Metacognitive Control Based on Learning Difficulty in a Perceptual Categorization Task

    ERIC Educational Resources Information Center

    Redford, Joshua S.

    2010-01-01

    Recently, Redford (2010) found that monkeys seemed to exert metacognitive control in a category-learning paradigm. Specifically, they selected more trials to view as the difficulty of the category-learning task increased. However, category-learning difficulty was determined by manipulating the family resemblance across the to-be-learned exemplars.…

  9. EDITORIAL: ECRH physics and technology in ITER

    NASA Astrophysics Data System (ADS)

    Luce, T. C.

    2008-05-01

    It is a great pleasure to introduce you to this special issue containing papers from the 4th IAEA Technical Meeting on ECRH Physics and Technology in ITER, which was held 6-8 June 2007 at the IAEA Headquarters in Vienna, Austria. The meeting was attended by more than 40 ECRH experts representing 13 countries and the IAEA. Presentations given at the meeting were placed into five separate categories EC wave physics: current understanding and extrapolation to ITER Application of EC waves to confinement and stability studies, including active control techniques for ITER Transmission systems/launchers: state of the art and ITER relevant techniques Gyrotron development towards ITER needs System integration and optimisation for ITER. It is notable that the participants took seriously the focal point of ITER, rather than simply contributing presentations on general EC physics and technology. The application of EC waves to ITER presents new challenges not faced in the current generation of experiments from both the physics and technology viewpoints. High electron temperatures and the nuclear environment have a significant impact on the application of EC waves. The needs of ITER have also strongly motivated source and launcher development. Finally, the demonstrated ability for precision control of instabilities or non-inductive current drive in addition to bulk heating to fusion burn has secured a key role for EC wave systems in ITER. All of the participants were encouraged to submit their contributions to this special issue, subject to the normal publication and technical merit standards of Nuclear Fusion. Almost half of the participants chose to do so; many of the others had been published in other publications and therefore could not be included in this special issue. The papers included here are a representative sample of the meeting. The International Advisory Committee also asked the three summary speakers from the meeting to supply brief written summaries (O. Sauter

  10. Model-based hierarchical reinforcement learning and human action control.

    PubMed

    Botvinick, Matthew; Weinstein, Ari

    2014-11-01

    Recent work has reawakened interest in goal-directed or 'model-based' choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour.

  11. Model-based hierarchical reinforcement learning and human action control

    PubMed Central

    Botvinick, Matthew; Weinstein, Ari

    2014-01-01

    Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

  12. Modes of Executive Control in Sequence Learning: From Stimulus-Based to Plan-Based Control

    ERIC Educational Resources Information Center

    Tubau, Elisabet; Hommel, Bernhard; Lopez-Moliner, Joan

    2007-01-01

    The authors argue that human sequential learning is often but not always characterized by a shift from stimulus- to plan-based action control. To diagnose this shift, they manipulated the frequency of 1st-order transitions in a repeated manual left-right sequence, assuming that performance is sensitive to frequency-induced biases under stimulus-…

  13. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  14. Memory and cognitive control circuits in mathematical cognition and learning.

    PubMed

    Menon, V

    2016-01-01

    Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

  15. Patients with Parkinson's disease learn to control complex systems via procedural as well as non-procedural learning.

    PubMed

    Osman, Magda; Wilkinson, Leonora; Beigi, Mazda; Castaneda, Cristina Sanchez; Jahanshahi, Marjan

    2008-01-01

    The striatum is considered to mediate some forms of procedural learning. Complex dynamic control (CDC) tasks involve an individual having to make a series of sequential decisions to achieve a specific outcome (e.g. learning to operate and control a car), and they involve procedural learning. The aim of this study was to test the hypothesis that patients with Parkinson's disease who have striatal dysfunction, are impaired on CDC tasks only when learning involves procedural learning. 26 patients with Parkinson's disease (PD) and 26 age-matched controls performed two CDC tasks, one in which training was observation-based (non-procedural), and a second in which training was action-based (procedural). Both groups were able to control the system to a specific criterion equally well, regardless of the training condition. However, when reporting their knowledge of the underlying structure of the system, both groups showed poorer accuracy when learning took place through observation-based compared with action-based training. Moreover, the controls' accuracy in reporting the underlying structure of the systems was superior to that of PD patients. The findings suggest that the striatal dysfunction in Parkinson's disease is not associated with impairment of procedural learning, regardless of whether the task involved procedural learning or not. It is possible that the learning and performance on CDC tasks are mediated by perceptual priming mechanisms in the neocortex. PMID:18440038

  16. Thalamic Control of Human Attention Driven by Memory and Learning

    PubMed Central

    de Bourbon-Teles, José; Bentley, Paul; Koshino, Saori; Shah, Kushal; Dutta, Agneish; Malhotra, Paresh; Egner, Tobias; Husain, Masud; Soto, David

    2014-01-01

    Summary The role of the thalamus in high-level cognition—attention, working memory (WM), rule-based learning, and decision making—remains poorly understood, especially in comparison to that of cortical frontoparietal networks [1–3]. Studies of visual thalamus have revealed important roles for pulvinar and lateral geniculate nucleus in visuospatial perception and attention [4–10] and for mediodorsal thalamus in oculomotor control [11]. Ventrolateral thalamus contains subdivisions devoted to action control as part of a circuit involving the basal ganglia [12, 13] and motor, premotor, and prefrontal cortices [14], whereas anterior thalamus forms a memory network in connection with the hippocampus [15]. This connectivity profile suggests that ventrolateral and anterior thalamus may represent a nexus between mnemonic and control functions, such as action or attentional selection. Here, we characterize the role of thalamus in the interplay between memory and visual attention. We show that ventrolateral lesions impair the influence of WM representations on attentional deployment. A subsequent fMRI study in healthy volunteers demonstrates involvement of ventrolateral and, notably, anterior thalamus in biasing attention through WM contents. To further characterize the memory types used by the thalamus to bias attention, we performed a second fMRI study that involved learning of stimulus-stimulus associations and their retrieval from long-term memory to optimize attention in search. Responses in ventrolateral and anterior thalamic nuclei tracked learning of the predictiveness of these abstract associations and their use in directing attention. These findings demonstrate a key role for human thalamus in higher-level cognition, notably, in mnemonic biasing of attention. PMID:24746799

  17. The physics role of ITER

    SciTech Connect

    Rutherford, P.H.

    1997-04-01

    Experimental research on the International Thermonuclear Experimental Reactor (ITER) will go far beyond what is possible on present-day tokamaks to address new and challenging issues in the physics of reactor-like plasmas. First and foremost, experiments in ITER will explore the physics issues of burning plasmas--plasmas that are dominantly self-heated by alpha-particles created by the fusion reactions themselves. Such issues will include (i) new plasma-physical effects introduced by the presence within the plasma of an intense population of energetic alpha particles; (ii) the physics of magnetic confinement for a burning plasma, which will involve a complex interplay of transport, stability and an internal self-generated heat source; and (iii) the physics of very-long-pulse/steady-state burning plasmas, in which much of the plasma current is also self-generated and which will require effective control of plasma purity and plasma-wall interactions. Achieving and sustaining burning plasma regimes in a tokamak necessarily requires plasmas that are larger than those in present experiments and have higher energy content and power flow, as well as much longer pulse length. Accordingly, the experimental program on ITER will embrace the study of issues of plasma physics and plasma-materials interactions that are specific to a reactor-scale fusion experiment. Such issues will include (i) confinement physics for a tokamak in which, for the first time, the core-plasma and the edge-plasma are simultaneously in a reactor-like regime; (ii) phenomena arising during plasma transients, including so-called disruptions, in regimes of high plasma current and thermal energy; and (iii) physics of a radiative divertor designed for handling high power flow for long pulses, including novel plasma and atomic-physics effects as well as materials science of surfaces subject to intense plasma interaction. Experiments on ITER will be conducted by researchers in control rooms situated at major

  18. Learning anticipation via spiking networks: application to navigation control.

    PubMed

    Arena, Paolo; Fortuna, Luigi; Frasca, Mattia; Patané, Luca

    2009-02-01

    In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning. PMID:19150797

  19. Learning anticipation via spiking networks: application to navigation control.

    PubMed

    Arena, Paolo; Fortuna, Luigi; Frasca, Mattia; Patané, Luca

    2009-02-01

    In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.

  20. Learned helplessness and learned prevalence: exploring the causal relations among perceived controllability, reward prevalence, and exploration.

    PubMed

    Teodorescu, Kinneret; Erev, Ido

    2014-10-01

    Exposure to uncontrollable outcomes has been found to trigger learned helplessness, a state in which the agent, because of lack of exploration, fails to take advantage of regained control. Although the implications of this phenomenon have been widely studied, its underlying cause remains undetermined. One can learn not to explore because the environment is uncontrollable, because the average reinforcement for exploring is low, or because rewards for exploring are rare. In the current research, we tested a simple experimental paradigm that contrasts the predictions of these three contributors and offers a unified psychological mechanism that underlies the observed phenomena. Our results demonstrate that learned helplessness is not correlated with either the perceived controllability of one's environment or the average reward, which suggests that reward prevalence is a better predictor of exploratory behavior than the other two factors. A simple computational model in which exploration decisions were based on small samples of past experiences captured the empirical phenomena while also providing a cognitive basis for feelings of uncontrollability.

  1. Loss of control during instrumental learning: a source localization study.

    PubMed

    Diener, Carsten; Kuehner, Christine; Flor, Herta

    2010-04-01

    This study used multi-channel electroencephalography (EEG) to investigate cortical correlates of response-outcome contingency appraisal as indexed by the postimperative negative variation (PINV) during instrumental learning. PINV data were subjected to standardized low resolution brain electromagnetic tomography (sLORETA) for source localization. Forty-six healthy adult persons underwent a forewarned S1-S2 paradigm where response-outcome contingencies varied in three consecutive conditions. Initially subjects could control aversive stimulation by a correct behavioral response followed by loss of control and subsequent restitution of control. Throughout the experiment, reaction times, errors, ratings of controllability, arousal, emotional valence and helplessness were assessed. Topographical EEG analyses showed that in particular frontal PINV magnitudes covaried with the experimental manipulation. Loss of control induced extensive response-outcome uncertainty accompanied by a fronto-central PINV maximum. sLORETA functional analyses of the PINV revealed that dependent on the experimental conditions frontal, temporal and parietal areas seem to be related to PINV formation. In particular during loss of control, between-conditions sLORETA comparisons found Brodmann Area 24 in the anterior cingulate cortex (ACC) to be associated with PINV generation, which was confirmed by correlational analyses. These results provide further evidence for the role of the ACC in detecting response conflict and its involvement in the generation of the PINV.

  2. Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model

    PubMed Central

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F.

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources – expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human–robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to “cheating” by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world. PMID:21808619

  3. Robot cognitive control with a neurophysiologically inspired reinforcement learning model.

    PubMed

    Khamassi, Mehdi; Lallée, Stéphane; Enel, Pierre; Procyk, Emmanuel; Dominey, Peter F

    2011-01-01

    A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real-world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e., cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources - expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real-world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human-robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to "cheating" by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real-world.

  4. A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, Juan A.; Gonzalez, Evelio J.

    2010-01-01

    As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

  5. Machine Learning for Quantum Metrology and Quantum Control

    NASA Astrophysics Data System (ADS)

    Sanders, Barry; Zahedinejad, Ehsan; Palittapongarnpim, Pantita

    Generating quantum metrological procedures and quantum gate designs, subject to constraints such as temporal or particle-number bounds or limits on the number of control parameters, are typically hard computationally. Although greedy machine learning algorithms are ubiquitous for tackling these problems, the severe constraints listed above limit the efficacy of such approaches. Our aim is to devise heuristic machine learning techniques to generate tractable procedures for adaptive quantum metrology and quantum gate design. In particular we have modified differential evolution to generate adaptive interferometric-phase quantum metrology procedures for up to 100 photons including loss and noise, and we have generated policies for designing single-shot high-fidelity three-qubit gates in superconducting circuits by avoided level crossings. Although quantum metrology and quantum control are regarded as disparate, we have developed a unified framework for these two subjects, and this unification enables us to transfer insights and breakthroughs from one of the topics to the other. Thanks to NSERC, AITF and 1000 Talent Plan.

  6. Neutron activation for ITER

    SciTech Connect

    Barnes, C.W.; Loughlin, M.J.; Nishitani, Takeo

    1996-04-29

    There are three primary goals for the Neutron Activation system for ITER: maintain a robust relative measure of fusion power with stability and high dynamic range (7 orders of magnitude); allow an absolute calibration of fusion power (energy); and provide a flexible and reliable system for materials testing. The nature of the activation technique is such that stability and high dynamic range can be intrinsic properties of the system. It has also been the technique that demonstrated (on JET and TFTR) the highest accuracy neutron measurements in DT operation. Since the gamma-ray detectors are not located on the tokamak and are therefore amenable to accurate characterization, and if material foils are placed very close to the ITER plasma with minimum scattering or attenuation, high overall accuracy in the fusion energy production (7--10%) should be achievable on ITER. In the paper, a conceptual design is presented. A system is shown to be capable of meeting these three goals, also detailed design issues remain to be solved.

  7. Autonomy Supported, Learner-Controlled or System-Controlled Learning in Hypermedia Environments and the Influence of Academic Self-Regulation Style

    ERIC Educational Resources Information Center

    Gorissen, Chantal J. J.; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

    2015-01-01

    This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This research was performed to gain more insight in the…

  8. Learning control for slewing of a flexible panel. [large space structures

    NASA Technical Reports Server (NTRS)

    Phan, M.; Longman, R. W.; Wei, Y.; Horta, L. G.; Juang, J.-N.

    1989-01-01

    This paper studies the applicability of a discrete-time learning control method to the slewing control of a large flexible panel. Among the issues discussed are feasibility of the desired trajectories and their specification schemes, learning control by linear feedback, limitation of the actuator device output, and robustness to parameter changes or modeling errors associated with the chosen learning control design. To demonstrate the effectiveness of learning control, the system is designed with a proportional controller of the base angle only. Then application of the learning control is shown to make the system learn quickly to achieve the desired slewing without residual vibration at the end of the maneuver. Simulation results are reported and discussed.

  9. A self-learning call admission control scheme for CDMA cellular networks.

    PubMed

    Liu, Derong; Zhang, Yi; Zhang, Huaguang

    2005-09-01

    In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms. PMID:16252828

  10. A novel resistance iterative algorithm for CCOS

    NASA Astrophysics Data System (ADS)

    Zheng, Ligong; Zhang, Xuejun

    2006-08-01

    CCOS (Computer Control Optical Surfacing) technology is widely used for making aspheric mirrors. For most manufacturers, dwell time algorithm is usually employed to determine the route and dwell time of the small tools to converge the errors. In this article, a novel damp iterative algorithm is proposed. We chose revolutions of the small tool instead of dwell time to determine fabrication stratagem. By using resistance iterative algorithm, we can solve these revolutions. Several mirrors have been manufactured by this method, all of them have fulfilled the demand of the designers, a 1m aspheric mirror was finished within 3 months.

  11. ETR/ITER systems code

    SciTech Connect

    Barr, W.L.; Bathke, C.G.; Brooks, J.N.; Bulmer, R.H.; Busigin, A.; DuBois, P.F.; Fenstermacher, M.E.; Fink, J.; Finn, P.A.; Galambos, J.D.; Gohar, Y.; Gorker, G.E.; Haines, J.R.; Hassanein, A.M.; Hicks, D.R.; Ho, S.K.; Kalsi, S.S.; Kalyanam, K.M.; Kerns, J.A.; Lee, J.D.; Miller, J.R.; Miller, R.L.; Myall, J.O.; Peng, Y-K.M.; Perkins, L.J.; Spampinato, P.T.; Strickler, D.J.; Thomson, S.L.; Wagner, C.E.; Willms, R.S.; Reid, R.L.

    1988-04-01

    A tokamak systems code capable of modeling experimental test reactors has been developed and is described in this document. The code, named TETRA (for Tokamak Engineering Test Reactor Analysis), consists of a series of modules, each describing a tokamak system or component, controlled by an optimizer/driver. This code development was a national effort in that the modules were contributed by members of the fusion community and integrated into a code by the Fusion Engineering Design Center. The code has been checked out on the Cray computers at the National Magnetic Fusion Energy Computing Center and has satisfactorily simulated the Tokamak Ignition/Burn Experimental Reactor II (TIBER) design. A feature of this code is the ability to perform optimization studies through the use of a numerical software package, which iterates prescribed variables to satisfy a set of prescribed equations or constraints. This code will be used to perform sensitivity studies for the proposed International Thermonuclear Experimental Reactor (ITER). 22 figs., 29 tabs.

  12. A Robust Reinforcement Learning Control Design Method for Nonlinear System with Partially Unknown Structure

    NASA Astrophysics Data System (ADS)

    Nakano, Kazuhiro; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

    We propose a robust control system which has robustness for disturbance and can deal with a nonlinear system with partially unknown structure by fusing reinforcement learning and robust control theory. First, we solved an optimal control problem without using unknown part of functions of the system, using neural network and the repetition learning of reinforcement learning algorithm. Second, we built the robust reinforcement learning control system which permits uncertainty and has robustness for disturbance by fusing the idea of H infinity control theory with above system.

  13. Multi-strategy learning of search control for partial-order planning

    SciTech Connect

    Estlin, T.A.; Mooney, R.J.

    1996-12-31

    Most research in planning and learning has involved linear, state-based planners. This paper presents SCOPE, a system for learning search-control rules that improve the performance of a partial-order planner. SCOPE integrates explanation-based and inductive learning techniques to acquire control rules for a partial-order planner. Learned rules are in the form of selection heuristics that help the planner choose between competing plan refinements. Specifically, SCOPE learns domain-specific control rules for a version of the UCPOP planning algorithm. The resulting system is shown to produce significant speedup in two different planning domains.

  14. Fast online learning of control regime transitions for adaptive robotic mobility

    NASA Astrophysics Data System (ADS)

    Yamauchi, Brian

    2012-06-01

    We introduce a new framework, Model Transition Control (MTC), that models robot control problems as sets of linear control regimes linked by nonlinear transitions, and a new learning algorithm, Dynamic Threshold Learning (DTL), that learns the boundaries of these control regimes in real-time. We demonstrate that DTL can learn to prevent understeer and oversteer while controlling a simulated high-speed vehicle. We also show that DTL can enable an iRobot PackBot to avoid rollover in rough terrain and to actively shift its center-of-gravity to maintain balance when climbing obstacles. In all cases, DTL is able to learn control regime boundaries in a few minutes, often with single-digit numbers of learning trials.

  15. Language learning and control in monolinguals and bilinguals.

    PubMed

    Bartolotti, James; Marian, Viorica

    2012-08-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals' mouse movements were attracted to native-language competitors, whereas bilinguals overcame competitor interference by increasing the activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly learned language.

  16. The Network Operations Control Center upgrade task: Lessons learned

    NASA Technical Reports Server (NTRS)

    Sherif, J. S.; Tran, T.-L.; Lee, S.

    1994-01-01

    This article synthesizes and describes the lessons learned from the Network Operations Control Center (NOCC) upgrade project, from the requirements phase through development and test and transfer. At the outset, the NOCC upgrade was being performed simultaneously with two other interfacing and dependent upgrades at the Signal Processing Center (SPC) and Ground Communications Facility (GCF), thereby adding a significant measure of complexity to the management and overall coordination of the development and transfer-to-operations (DTO) effort. Like other success stories, this project carried with it the traditional elements of top management support and exceptional dedication of cognizant personnel. Additionally, there were several NOCC-specific reasons for success, such as end-to-end system engineering, adoption of open-system architecture, thorough requirements management, and use of appropriate off-the-shelf technologies. On the other hand, there were several difficulties, such as ill-defined external interfaces, transition issues caused by new communications protocols, ambivalent use of two sets of policies and standards, and mistailoring of the new JPL management standard (due to the lack of practical guidelines). This article highlights the key lessons learned, as a means of constructive suggestions for the benefit of future projects.

  17. Language Learning and Control in Monolinguals and Bilinguals

    PubMed Central

    Bartolotti, James; Marian, Viorica

    2012-01-01

    Parallel language activation in bilinguals leads to competition between languages. Experience managing this interference may aid novel language learning by improving the ability to suppress competition from known languages. To investigate the effect of bilingualism on the ability to control native-language interference, monolinguals and bilinguals were taught an artificial language designed to elicit between-language competition. Partial activation of interlingual competitors was assessed with eye-tracking and mouse-tracking during a word recognition task in the novel language. Eye-tracking results showed that monolinguals looked at competitors more than bilinguals, and for a longer duration of time. Mouse-tracking results showed that monolinguals’ mouse-movements were attracted to native-language competitors, while bilinguals overcame competitor interference by increasing activation of target items. Results suggest that bilinguals manage cross-linguistic interference more effectively than monolinguals. We conclude that language interference can affect lexical retrieval, but bilingualism may reduce this interference by facilitating access to a newly-learned language. PMID:22462514

  18. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN. PMID:18632393

  19. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.

  20. Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

    ERIC Educational Resources Information Center

    Wu, Will F. W.; Magill, Richard A.

    2011-01-01

    For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

  1. Extortion can outperform generosity in the iterated prisoner's dilemma

    PubMed Central

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W.; Zheng, Jie; Xu, Bin

    2016-01-01

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513

  2. Extortion can outperform generosity in the iterated prisoner's dilemma.

    PubMed

    Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W; Zheng, Jie; Xu, Bin

    2016-01-01

    Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513

  3. PREFACE: Progress in the ITER Physics Basis

    NASA Astrophysics Data System (ADS)

    Ikeda, K.

    2007-06-01

    fundamental to its completion. I am pleased to witness the extensive collaborations, the excellent working relationships and the free exchange of views that have been developed among scientists working on magnetic fusion, and I would particularly like to acknowledge the importance which they assign to ITER in their research. This close collaboration and the spirit of free discussion will be essential to the success of ITER. Finally, the PIPB identifies issues which remain in the projection of burning plasma performance to the ITER scale and in the control of burning plasmas. Continued R&D is therefore called for to reduce the uncertainties associated with these issues and to ensure the efficient operation and exploitation of ITER. It is important that the international fusion community maintains a high level of collaboration in the future to address these issues and to prepare the physics basis for ITER operation. ITPA Coordination Committee R. Stambaugh (Chair of ITPA CC, General Atomics, USA) D.J. Campbell (Previous Chair of ITPA CC, European Fusion Development Agreement—Close Support Unit, ITER Organization) M. Shimada (Co-Chair of ITPA CC, ITER Organization) R. Aymar (ITER International Team, CERN) V. Chuyanov (ITER Organization) J.H. Han (Korea Basic Science Institute, Korea) Y. Huo (Zengzhou University, China) Y.S. Hwang (Seoul National University, Korea) N. Ivanov (Kurchatov Institute, Russia) Y. Kamada (Japan Atomic Energy Agency, Naka, Japan) P.K. Kaw (Institute for Plasma Research, India) S. Konovalov (Kurchatov Institute, Russia) M. Kwon (National Fusion Research Center, Korea) J. Li (Academy of Science, Institute of Plasma Physics, China) S. Mirnov (TRINITI, Russia) Y. Nakamura (National Institute for Fusion Studies, Japan) H. Ninomiya (Japan Atomic Energy Agency, Naka, Japan) E. Oktay (Department of Energy, USA) J. Pamela (European Fusion Development Agreement—Close Support Unit) C. Pan (Southwestern Institute of Physics, China) F. Romanelli (Ente per le

  4. Adaptive iterative reconstruction

    NASA Astrophysics Data System (ADS)

    Bruder, H.; Raupach, R.; Sunnegardh, J.; Sedlmair, M.; Stierstorfer, K.; Flohr, T.

    2011-03-01

    It is well known that, in CT reconstruction, Maximum A Posteriori (MAP) reconstruction based on a Poisson noise model can be well approximated by Penalized Weighted Least Square (PWLS) minimization based on a data dependent Gaussian noise model. We study minimization of the PWLS objective function using the Gradient Descent (GD) method, and show that if an exact inverse of the forward projector exists, the PWLS GD update equation can be translated into an update equation which entirely operates in the image domain. In case of non-linear regularization and arbitrary noise model this means that a non-linear image filter must exist which solves the optimization problem. In the general case of non-linear regularization and arbitrary noise model, the analytical computation is not trivial and might lead to image filters which are computationally very expensive. We introduce a new iteration scheme in image space, based on a regularization filter with an anisotropic noise model. Basically, this approximates the statistical data weighting and regularization in PWLS reconstruction. If needed, e.g. for compensation of the non-exactness of backprojector, the image-based regularization loop can be preceded by a raw data based loop without regularization and statistical data weighting. We call this combined iterative reconstruction scheme Adaptive Iterative Reconstruction (AIR). It will be shown that in terms of low-contrast visibility, sharpness-to-noise and contrast-to-noise ratio, PWLS and AIR reconstruction are similar to a high degree of accuracy. In clinical images the noise texture of AIR is also superior to the more artificial texture of PWLS.

  5. Overview on Experiments On ITER-like Antenna On JET And ICRF Antenna Design For ITER

    SciTech Connect

    Nightingale, M. P. S.; Blackman, T.; Edwards, D.; Fanthome, J.; Graham, M.; Hamlyn-Harris, C.; Hancock, D.; Jacquet, P.; Mayoral, M.-L.; Monakhov, I.; Nicholls, K.; Stork, D.; Whitehurst, A.; Wilson, D.; Wooldridge, E.

    2009-11-26

    Following an overview of the ITER Ion Cyclotron Resonance Frequency (ICRF) system, the JET ITER-like antenna (ILA) will be described. The ILA was designed to test the following ITER issues: (a) reliable operation at power densities of order 8 MW/m{sup 2} at voltages up to 45 kV using a close-packed array of straps; (b) powering through ELMs using an internal (in-vacuum) conjugate-T junction; (c) protection from arcing in a conjugate-T configuration, using both existing and novel systems; and (d) resilience to disruption forces. ITER-relevant results have been achieved: operation at high coupled power density; control of the antenna matching elements in the presence of high inter-strap coupling, use of four conjugate-T systems (as would be used in ITER, should a conjugate-T approach be used); operation with RF voltages on the antenna structures up to 42 kV; achievement of ELM tolerance with a conjugate-T configuration by operating at 3{omega} real impedance at the conjugate-T point; and validation of arc detection systems on conjugate-T configurations in ELMy H-mode plasmas. The impact of these results on the predicted performance and design of the ITER antenna will be reviewed. In particular, the implications of the RF coupling measured on JET will be discussed.

  6. Nuclear Instrumentation and Control Cyber Testbed Considerations – Lessons Learned

    SciTech Connect

    Jonathan Gray; Robert Anderson; Julio G. Rodriguez; Cheol-Kwon Lee

    2014-08-01

    Abstract: Identifying and understanding digital instrumentation and control (I&C) cyber vulnerabilities within nuclear power plants and other nuclear facilities, is critical if nation states desire to operate nuclear facilities safely, reliably, and securely. In order to demonstrate objective evidence that cyber vulnerabilities have been adequately identified and mitigated, a testbed representing a facility’s critical nuclear equipment must be replicated. Idaho National Laboratory (INL) has built and operated similar testbeds for common critical infrastructure I&C for over ten years. This experience developing, operating, and maintaining an I&C testbed in support of research identifying cyber vulnerabilities has led the Korean Atomic Energy Research Institute of the Republic of Korea to solicit the experiences of INL to help mitigate problems early in the design, development, operation, and maintenance of a similar testbed. The following information will discuss I&C testbed lessons learned and the impact of these experiences to KAERI.

  7. Quantum iterated function systems.

    PubMed

    Łoziński, Artur; Zyczkowski, Karol; Słomczyński, Wojciech

    2003-10-01

    An iterated function system (IFS) is defined by specifying a set of functions in a classical phase space, which act randomly on an initial point. In an analogous way, we define a quantum IFS (QIFS), where functions act randomly with prescribed probabilities in the Hilbert space. In a more general setting, a QIFS consists of completely positive maps acting in the space of density operators. This formalism is designed to describe certain problems of nonunitary quantum dynamics. We present exemplary classical IFSs, the invariant measure of which exhibits fractal structure, and study properties of the corresponding QIFSs and their invariant states.

  8. Iterative Magnetometer Calibration

    NASA Technical Reports Server (NTRS)

    Sedlak, Joseph

    2006-01-01

    This paper presents an iterative method for three-axis magnetometer (TAM) calibration that makes use of three existing utilities recently incorporated into the attitude ground support system used at NASA's Goddard Space Flight Center. The method combines attitude-independent and attitude-dependent calibration algorithms with a new spinning spacecraft Kalman filter to solve for biases, scale factors, nonorthogonal corrections to the alignment, and the orthogonal sensor alignment. The method is particularly well-suited to spin-stabilized spacecraft, but may also be useful for three-axis stabilized missions given sufficient data to provide observability.

  9. MLP iterative construction algorithm

    NASA Astrophysics Data System (ADS)

    Rathbun, Thomas F.; Rogers, Steven K.; DeSimio, Martin P.; Oxley, Mark E.

    1997-04-01

    The MLP Iterative Construction Algorithm (MICA) designs a Multi-Layer Perceptron (MLP) neural network as it trains. MICA adds Hidden Layer Nodes one at a time, separating classes on a pair-wise basis, until the data is projected into a linear separable space by class. Then MICA trains the Output Layer Nodes, which results in an MLP that achieves 100% accuracy on the training data. MICA, like Backprop, produces an MLP that is a minimum mean squared error approximation of the Bayes optimal discriminant function. Moreover, MICA's training technique yields novel feature selection technique and hidden node pruning technique

  10. Lessons learned from the MIT Tara control and data system

    SciTech Connect

    Gaudreau, M.P.J.; Sullivan, J.D.; Fredian, T.W.; Irby, J.H.; Karcher, C.A.; Rameriz, R.A.; Sevillano, E.; Stillerman, J.A.; Thomas, P.

    1987-10-01

    The control and data system of the MIT Tara Tandem Mirror has worked successfully throughout the lifetime of the experiment (1983 through 1987). As the Tara project winds down, it is appropriate to summarize the lessons learned from the implementation and operation of the control and data system over the years and in its final form. The control system handled approx.2400 I/0 points in real time throughout the 5 to 10 minute shot cycle while the data system, in near real time, handled approx.1000 signals with a total of 5 to 7 Mbytes of data each shot. The implementation depended upon a consistent approach based on separating physics and engineering functions and on detailed functional diagrams with narrowly defined cross communication. This paper is a comprehensive treatment of the principal successes, residual problems, and dilemmas that arose from the beginning until the final hardware and software implementation. Suggestions for future systems of either similar size or of larger scale such as CIT are made in the conclusion. 11 refs., 1 fig.

  11. Preliminary consideration of CFETR ITER-like case diagnostic system

    NASA Astrophysics Data System (ADS)

    Li, G. S.; Yang, Y.; Wang, Y. M.; Ming, T. F.; Han, X.; Liu, S. C.; Wang, E. H.; Liu, Y. K.; Yang, W. J.; Li, G. Q.; Hu, Q. S.; Gao, X.

    2016-11-01

    Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

  12. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    ERIC Educational Resources Information Center

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  13. Sustaining Teacher Control in a Blog-Based Personal Learning Environment

    ERIC Educational Resources Information Center

    Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

    2013-01-01

    Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

  14. A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality

    ERIC Educational Resources Information Center

    Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila

    2016-01-01

    Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

  15. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    PubMed Central

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  16. Learning in the Digital Age: Control or Connection?

    ERIC Educational Resources Information Center

    Van Galen, Jane

    2013-01-01

    In October 2011, 200 state school officers and legislators gathered at a hotel in San Francisco to learn how to "revolutionize" learning by "personalizing" instruction. The occasion was former Florida Gov. Jeb Bush's second annual National Summit on Education Reform. The topic was digital learning. The vision of digitally managed curriculum and…

  17. Learning To Control Democratically: Ethical Questions in Situated Adult Education.

    ERIC Educational Resources Information Center

    Heaney, Tom

    Lave and Wenger (1991) reject individualistic and psychologistic theories of learning in favor of a more broadly social and contextual approach. They observe that all learning is situated not only in space and time, but also inextricably in relation to social practice. Learning is "legitimate peripheral participation in a community of practice."…

  18. Towards Greater Learner Control: Web Supported Project-Based Learning

    ERIC Educational Resources Information Center

    Guthrie, Cameron

    2010-01-01

    Project-based learning has been suggested as an appropriate pedagogy to prepare students in information systems for the realities of the business world. Web-based resources have been used to support such pedagogy with mixed results. The paper argues that the design of web-based learning support to cater to different learning styles may give…

  19. Off-policy reinforcement learning for H∞ control design.

    PubMed

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen

    2015-01-01

    The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system. PMID:25532162

  20. Active Prospective Control Is Required for Effective Sensorimotor Learning

    PubMed Central

    Snapp-Childs, Winona; Casserly, Elizabeth; Mon-Williams, Mark; Bingham, Geoffrey P.

    2013-01-01

    Passive modeling of movements is often used in movement therapy to overcome disabilities caused by stroke or other disorders (e.g. Developmental Coordination Disorder or Cerebral Palsy). Either a therapist or, recently, a specially designed robot moves or guides the limb passively through the movement to be trained. In contrast, action theory has long suggested that effective skill acquisition requires movements to be actively generated. Is this true? In view of the former, we explicitly tested the latter. Previously, a method was developed that allows children with Developmental Coordination Disorder to produce effective movements actively, so as to improve manual performance to match that of typically developing children. In the current study, we tested practice using such active movements as compared to practice using passive movement. The passive movement employed, namely haptic tracking, provided a strong test of the comparison, one that showed that the mere inaction of the muscles is not the problem. Instead, lack of prospective control was. The result was no effective learning with passive movement while active practice with prospective control yielded significant improvements in performance. PMID:24194891

  1. Off-policy reinforcement learning for H∞ control design.

    PubMed

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen

    2015-01-01

    The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.

  2. Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    PubMed

    Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

    2015-02-01

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks. PMID:26353251

  3. Precise feedback control underlies sensorimotor learning in speech.

    PubMed

    Vaughn, Chris; Nasir, Sazzad M

    2015-02-01

    Acquiring the skill of speaking in another language, or for that matter a child's learning to talk, does not follow a single recipe. People learn by variable amounts. A major component of speech learnability seems to be sensing precise feedback errors to correct subsequent utterances that help maintain speech goals. We have tested this idea in a speech motor learning paradigm under altered auditory feedback, in which subjects repeated a word while their auditory feedback was changed online. Subjects learned the task to variable degrees, with some simply failing to learn. We assessed feedback contribution by computing one-lag covariance between formant trajectories of the current feedback and the following utterance that was found to be a significant predictor of learning. Our findings rely on a novel use of information-rich formant trajectories in evaluating speech motor learning and argue for their relevance in auditory speech goals of vowel sounds.

  4. Challenges and status of ITER conductor production

    NASA Astrophysics Data System (ADS)

    Devred, A.; Backbier, I.; Bessette, D.; Bevillard, G.; Gardner, M.; Jong, C.; Lillaz, F.; Mitchell, N.; Romano, G.; Vostner, A.

    2014-04-01

    Taking the relay of the large Hadron collider (LHC) at CERN, ITER has become the largest project in applied superconductivity. In addition to its technical complexity, ITER is also a management challenge as it relies on an unprecedented collaboration of seven partners, representing more than half of the world population, who provide 90% of the components as in-kind contributions. The ITER magnet system is one of the most sophisticated superconducting magnet systems ever designed, with an enormous stored energy of 51 GJ. It involves six of the ITER partners. The coils are wound from cable-in-conduit conductors (CICCs) made up of superconducting and copper strands assembled into a multistage cable, inserted into a conduit of butt-welded austenitic steel tubes. The conductors for the toroidal field (TF) and central solenoid (CS) coils require about 600 t of Nb3Sn strands while the poloidal field (PF) and correction coil (CC) and busbar conductors need around 275 t of Nb-Ti strands. The required amount of Nb3Sn strands far exceeds pre-existing industrial capacity and has called for a significant worldwide production scale up. The TF conductors are the first ITER components to be mass produced and are more than 50% complete. During its life time, the CS coil will have to sustain several tens of thousands of electromagnetic (EM) cycles to high current and field conditions, way beyond anything a large Nb3Sn coil has ever experienced. Following a comprehensive R&D program, a technical solution has been found for the CS conductor, which ensures stable performance versus EM and thermal cycling. Productions of PF, CC and busbar conductors are also underway. After an introduction to the ITER project and magnet system, we describe the ITER conductor procurements and the quality assurance/quality control programs that have been implemented to ensure production uniformity across numerous suppliers. Then, we provide examples of technical challenges that have been encountered and

  5. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  6. Iterated crowdsourcing dilemma game

    NASA Astrophysics Data System (ADS)

    Oishi, Koji; Cebrian, Manuel; Abeliuk, Andres; Masuda, Naoki

    2014-02-01

    The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space.

  7. Learning-based position control of a closed-kinematic chain robot end-effector

    NASA Technical Reports Server (NTRS)

    Nguyen, Charles C.; Zhou, Zhen-Lei

    1990-01-01

    A trajectory control scheme whose design is based on learning theory, for a six-degree-of-freedom (DOF) robot end-effector built to study robotic assembly of NASA hardwares in space is presented. The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system is designed using the concept of linearization about a selected operating point, and the method of pole placement so that the closed-loop linearized system is stabilized. The learning control scheme consisting of PD-type learning controllers, provides additional inputs to improve the end-effector performance after each trial. Experimental studies performed on a 2 DOF end-effector built at CUA, for three tracking cases show that actual trajectories approach desired trajectories as the number of trials increases. The tracking errors are substantially reduced after only five trials.

  8. Motor Learning and Control Foundations of Kinesiology: Defining the Academic Core

    ERIC Educational Resources Information Center

    Fischman, Mark G.

    2007-01-01

    This paper outlines the kinesiological foundations of the motor behavior subdisciplines of motor learning and motor control. After defining the components of motor behavior, the paper addresses the undergraduate major and core knowledge by examining several classic textbooks in motor learning and control, as well as a number of contemporary…

  9. Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments.

    ERIC Educational Resources Information Center

    Baylor, Amy L.

    2001-01-01

    Discussion of intelligent agents and their use in computer learning environments focuses on cognitive considerations. Presents four dimension of control that should be considered in designing agent-based learning environments: learner control, from constructivist to instructivist; feedback; relationship of learner to agent; and learner confidence…

  10. Self-Controlled Amount of Practice Benefits Learning of a Motor Skill

    ERIC Educational Resources Information Center

    Post, Phillip G.; Fairbrother, Jeffrey T.; Barros, Joao A. C.

    2011-01-01

    Self-control over factors involving task-related information (e.g., feedback) can enhance motor learning. It is unknown if these benefits extend to manipulations that do not directly affect such information. The purpose of this study was to determine if self-control over the amount of practice would also facilitate learning. Participants learned…

  11. Perceived Control and Adaptive Coping: Programs for Adolescent Students Who Have Learning Disabilities

    ERIC Educational Resources Information Center

    Firth, Nola; Frydenberg, Erica; Greaves, Daryl

    2008-01-01

    This study explored the effect of a coping program and a teacher feedback intervention on perceived control and adaptive coping for 98 adolescent students who had specific learning disabilities. The coping program was modified to build personal control and to address the needs of students who have specific learning disabilities. The teacher…

  12. Ordinal neural networks without iterative tuning.

    PubMed

    Fernández-Navarro, Francisco; Riccardi, Annalisa; Carloni, Sante

    2014-11-01

    Ordinal regression (OR) is an important branch of supervised learning in between the multiclass classification and regression. In this paper, the traditional classification scheme of neural network is adapted to learn ordinal ranks. The model proposed imposes monotonicity constraints on the weights connecting the hidden layer with the output layer. To do so, the weights are transcribed using padding variables. This reformulation leads to the so-called inequality constrained least squares (ICLS) problem. Its numerical solution can be obtained by several iterative methods, for example, trust region or line search algorithms. In this proposal, the optimum is determined analytically according to the closed-form solution of the ICLS problem estimated from the Karush-Kuhn-Tucker conditions. Furthermore, following the guidelines of the extreme learning machine framework, the weights connecting the input and the hidden layers are randomly generated, so the final model estimates all its parameters without iterative tuning. The model proposed achieves competitive performance compared with the state-of-the-art neural networks methods for OR. PMID:25330430

  13. Learning feedback and feedforward control in a mirror-reversed visual environment.

    PubMed

    Kasuga, Shoko; Telgen, Sebastian; Ushiba, Junichi; Nozaki, Daichi; Diedrichsen, Jörn

    2015-10-01

    When we learn a novel task, the motor system needs to acquire both feedforward and feedback control. Currently, little is known about how the learning of these two mechanisms relate to each other. In the present study, we tested whether feedforward and feedback control need to be learned separately, or whether they are learned as common mechanism when a new control policy is acquired. Participants were trained to reach to two lateral and one central target in an environment with mirror (left-right)-reversed visual feedback. One group was allowed to make online movement corrections, whereas the other group only received visual information after the end of the movement. Learning of feedforward control was assessed by measuring the accuracy of the initial movement direction to lateral targets. Feedback control was measured in the responses to sudden visual perturbations of the cursor when reaching to the central target. Although feedforward control improved in both groups, it was significantly better when online corrections were not allowed. In contrast, feedback control only adaptively changed in participants who received online feedback and remained unchanged in the group without online corrections. Our findings suggest that when a new control policy is acquired, feedforward and feedback control are learned separately, and that there may be a trade-off in learning between feedback and feedforward controllers. PMID:26245313

  14. A Learning Model for Enhancing the Student's Control in Educational Process Using Web 2.0 Personal Learning Environments

    ERIC Educational Resources Information Center

    Rahimi, Ebrahim; van den Berg, Jan; Veen, Wim

    2015-01-01

    In recent educational literature, it has been observed that improving student's control has the potential of increasing his or her feeling of ownership, personal agency and activeness as means to maximize his or her educational achievement. While the main conceived goal for personal learning environments (PLEs) is to increase student's control by…

  15. Wall conditioning for ITER: Current experimental and modeling activities

    NASA Astrophysics Data System (ADS)

    Douai, D.; Kogut, D.; Wauters, T.; Brezinsek, S.; Hagelaar, G. J. M.; Hong, S. H.; Lomas, P. J.; Lyssoivan, A.; Nunes, I.; Pitts, R. A.; Rohde, V.; de Vries, P. C.

    2015-08-01

    Wall conditioning will be required in ITER to control fuel and impurity recycling, as well as tritium (T) inventory. Analysis of conditioning cycle on the JET, with its ITER-Like Wall is presented, evidencing reduced need for wall cleaning in ITER compared to JET-CFC. Using a novel 2D multi-fluid model, current density during Glow Discharge Conditioning (GDC) on the in-vessel plasma-facing components (PFC) of ITER is predicted to approach the simple expectation of total anode current divided by wall surface area. Baking of the divertor to 350 °C should desorb the majority of the co-deposited T. ITER foresees the use of low temperature plasma based techniques compatible with the permanent toroidal magnetic field, such as Ion (ICWC) or Electron Cyclotron Wall Conditioning (ECWC), for tritium removal between ITER plasma pulses. Extrapolation of JET ICWC results to ITER indicates removal comparable to estimated T-retention in nominal ITER D:T shots, whereas GDC may be unattractive for that purpose.

  16. On the integration of reinforcement learning and approximate reasoning for control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

  17. Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems

    PubMed Central

    Chen, Sanfeng; Li, Shuai; Liu, Bo; Lou, Yuesheng; Liang, Yongsheng

    2012-01-01

    Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. PMID:22778633

  18. Hierarchical approximate policy iteration with binary-tree state space decomposition.

    PubMed

    Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen

    2011-12-01

    In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.

  19. CREB Selectively Controls Learning-Induced Structural Remodeling of Neurons

    ERIC Educational Resources Information Center

    Middei, Silvia; Spalloni, Alida; Longone, Patrizia; Pittenger, Christopher; O'Mara, Shane M.; Marie, Helene; Ammassari-Teule, Martine

    2012-01-01

    The modulation of synaptic strength associated with learning is post-synaptically regulated by changes in density and shape of dendritic spines. The transcription factor CREB (cAMP response element binding protein) is required for memory formation and in vitro dendritic spine rearrangements, but its role in learning-induced remodeling of neurons…

  20. Remote Labs and Game-Based Learning for Process Control

    ERIC Educational Resources Information Center

    Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

    2013-01-01

    Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

  1. Approximate Optimal Control as a Model for Motor Learning

    ERIC Educational Resources Information Center

    Berthier, Neil E.; Rosenstein, Michael T.; Barto, Andrew G.

    2005-01-01

    Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of…

  2. Comparative Learning in Partnerships: Control, Competition or Collaboration?

    ERIC Educational Resources Information Center

    Takahashi, Chie

    2008-01-01

    This paper examines the quality and development of relations between organisations and the ways in which these are informed by incidental learning experiences in two projects. The paper conceptualizes instances of inter-organisational learning (IOL) applying theories such as principal-agent, prisoners' dilemma and women's place in community…

  3. Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

    ERIC Educational Resources Information Center

    Kelly, Declan

    2008-01-01

    Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

  4. Remote Learning for the Manipulation and Control of Robotic Cells

    ERIC Educational Resources Information Center

    Goldstain, Ofir; Ben-Gal, Irad; Bukchin, Yossi

    2007-01-01

    This work proposes an approach to remote learning of robotic cells based on internet and simulation tools. The proposed approach, which integrates remote-learning and tele-operation into a generic scheme, is designed to enable students and developers to set-up and manipulate a robotic cell remotely. Its implementation is based on a dedicated…

  5. Assessing Students' Learning of Internal Controls: Closing the Loop

    ERIC Educational Resources Information Center

    Amer, T. S.; Mohrweis, Lawrence C.

    2009-01-01

    This study describes the multifaceted components of an assessment process. The paper explains a novel approach in which an advisory council participated in a "fun," hands-on activity to rank-order learning outcomes. The top ranked learning competency, as identified by the advisory council, was the need for students to gain a better…

  6. Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

    PubMed

    Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

    2006-01-01

    Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

  7. Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

    PubMed

    Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

    2006-01-01

    Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions. PMID:16806313

  8. Online kernel-based learning for task-space tracking robot control.

    PubMed

    Nguyen-Tuong, Duy; Peters, Jan

    2012-09-01

    Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.

  9. Longitudinal investigation on learned helplessness tested under negative and positive reinforcement involving stimulus control.

    PubMed

    Oliveira, Emileane C; Hunziker, Maria Helena

    2014-07-01

    In this study, we investigated whether (a) animals demonstrating the learned helplessness effect during an escape contingency also show learning deficits under positive reinforcement contingencies involving stimulus control and (b) the exposure to positive reinforcement contingencies eliminates the learned helplessness effect under an escape contingency. Rats were initially exposed to controllable (C), uncontrollable (U) or no (N) shocks. After 24h, they were exposed to 60 escapable shocks delivered in a shuttlebox. In the following phase, we selected from each group the four subjects that presented the most typical group pattern: no escape learning (learned helplessness effect) in Group U and escape learning in Groups C and N. All subjects were then exposed to two phases, the (1) positive reinforcement for lever pressing under a multiple FR/Extinction schedule and (2) a re-test under negative reinforcement (escape). A fourth group (n=4) was exposed only to the positive reinforcement sessions. All subjects showed discrimination learning under multiple schedule. In the escape re-test, the learned helplessness effect was maintained for three of the animals in Group U. These results suggest that the learned helplessness effect did not extend to discriminative behavior that is positively reinforced and that the learned helplessness effect did not revert for most subjects after exposure to positive reinforcement. We discuss some theoretical implications as related to learned helplessness as an effect restricted to aversive contingencies and to the absence of reversion after positive reinforcement. This article is part of a Special Issue entitled: insert SI title.

  10. A mathematical theory of learning control for linear discrete multivariable systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Longman, Richard W.

    1988-01-01

    When tracking control systems are used in repetitive operations such as robots in various manufacturing processes, the controller will make the same errors repeatedly. Here consideration is given to learning controllers that look at the tracking errors in each repetition of the process and adjust the control to decrease these errors in the next repetition. A general formalism is developed for learning control of discrete-time (time-varying or time-invariant) linear multivariable systems. Methods of specifying a desired trajectory (such that the trajectory can actually be performed by the discrete system) are discussed, and learning controllers are developed. Stability criteria are obtained which are relatively easy to use to insure convergence of the learning process, and proper gain settings are discussed in light of measurement noise and system uncertainties.

  11. Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design.

    PubMed

    Sudareshan, M K; Condarcure, T A

    1998-01-01

    Training of recurrent neural networks by conventional error backpropagation methods introduces considerable computational complexities due to the need for gradient evaluations. In this paper we shall present a training approach using concepts from the theory of stochastic learning automata that eliminates the need for computation of gradients and hence affords a very simple implementation, particularly for implementation on low-end platforms such as personal computers. This approach also offers the flexibility of tailoring a number of specific training algorithms based on the selection of linear and nonlinear reinforcement rules for updating automaton action probabilities. The training efficiency is demonstrated by application to two complex temporal learning scenarios, viz. learning of time-dependent continuous trajectories and feedback controller designs for continuous dynamical plants. For the first problem, it is shown that training algorithms can be tailored following the present approach for a recurrent neural net to learn to generate a benchmark circular trajectory more accurately than possible with existing gradient-based training procedures. For the second problem, it is shown that recurrent neural-network-based feedback controllers can be trained for different control objectives (plant stabilization, output tracking) even when the plant being controlled has complex nonlinear dynamics, and that these controllers possess excellent robustness characteristics with respect to variations in the initial states of the dynamical plant being controlled.

  12. Iterating between Lessons on Concepts and Procedures Can Improve Mathematics Knowledge

    ERIC Educational Resources Information Center

    Rittle-Johnson, Bethany; Koedinger, Kenneth

    2009-01-01

    Background: Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. Aims: The purpose of the current study was to evaluate the…

  13. ITER Diagnostic First Wal

    SciTech Connect

    G. Douglas Loesser, et. al.

    2012-09-21

    The ITER Diagnostic Division is responsible for designing and procuring the First Wall Blankets that are mounted on the vacuum vessel port plugs at both the upper and equatorial levels This paper will discuss the effects of the diagnostic aperture shape and configuration on the coolant circuit design. The DFW design is driven in large part by the need to conform the coolant arrangement to a wide variety of diagnostic apertures combined with the more severe heating conditions at the surface facing the plasma, the first wall. At the first wall, a radiant heat flux of 35W/cm2 combines with approximate peak volumetric heating rates of 8W/cm3 (equatorial ports) and 5W/cm3 (upper ports). Here at the FW, a fast thermal response is desirable and leads to a thin element between the heat flux and coolant. This requirement is opposed by the wish for a thicker FW element to accommodate surface erosion and other off-normal plasma events.

  14. From Intent to Action: An Iterative Engineering Process

    ERIC Educational Resources Information Center

    Mouton, Patrice; Rodet, Jacques; Vacaresse, Sylvain

    2015-01-01

    Quite by chance, and over the course of a few haphazard meetings, a Master's degree in "E-learning Design" gradually developed in a Faculty of Economics. Its original and evolving design was the result of an iterative process carried out, not by a single Instructional Designer (ID), but by a full ID team. Over the last 10 years it has…

  15. Gamma ray spectrometer for ITER

    SciTech Connect

    Gin, D.; Chugunov, I.; Shevelev, A.; Khilkevitch, E.; Doinikov, D.; Naidenov, V.; Pasternak, A.; Polunovsky, I.; Kiptily, V.

    2014-08-21

    Gamma diagnostics is considered to be primary for the confined α-particles and runaway electrons measurements on ITER. The gamma spectrometer will be embedded into a neutron dump of the ITER Neutral Particle Analyzer diagnostic complex. It will supplement NPA measurements on the fuel isotope ratio and confined alphas/fast ions. In this paper an update on ITER gamma spectrometer developments is given. A new geometry of the system is described and detailed analysis of expected signals for the spectrometer is presented.

  16. Learning in the tutorial group: a balance between individual freedom and institutional control.

    PubMed

    McAllister, Anita; Aanstoot, Janna; Hammarström, Inger Lundeborg; Samuelsson, Christina; Johannesson, Eva; Sandström, Karin; Berglind, Ulrika

    2014-01-01

    The study investigates factors in problem-based learning tutorial groups which promote or inhibit learning. The informants were tutors and students from speech-language pathology and physiotherapy programmes. Semi-structured focus-group interviews and individual interviews were used. Results revealed three themes: Responsibility. Time and Support. Under responsibility, the delicate balance between individual and institutional responsibility and control was shown. Time included short and long-term perspectives on learning. Under support, supporting documents, activities and personnel resources were mentioned. In summary, an increased control by the program and tutors decreases student's motivation to assume responsibility for learning. Support in tutorial groups needs to adapt to student progression and to be well aligned to tutorial work to have the intended effect. A lifelong learning perspective may help students develop a meta-awareness regarding learning that could make tutorial work more meaningful. PMID:23848371

  17. Learning in the tutorial group: a balance between individual freedom and institutional control.

    PubMed

    McAllister, Anita; Aanstoot, Janna; Hammarström, Inger Lundeborg; Samuelsson, Christina; Johannesson, Eva; Sandström, Karin; Berglind, Ulrika

    2014-01-01

    The study investigates factors in problem-based learning tutorial groups which promote or inhibit learning. The informants were tutors and students from speech-language pathology and physiotherapy programmes. Semi-structured focus-group interviews and individual interviews were used. Results revealed three themes: Responsibility. Time and Support. Under responsibility, the delicate balance between individual and institutional responsibility and control was shown. Time included short and long-term perspectives on learning. Under support, supporting documents, activities and personnel resources were mentioned. In summary, an increased control by the program and tutors decreases student's motivation to assume responsibility for learning. Support in tutorial groups needs to adapt to student progression and to be well aligned to tutorial work to have the intended effect. A lifelong learning perspective may help students develop a meta-awareness regarding learning that could make tutorial work more meaningful.

  18. Learning control for minimizing a quadratic cost during repetitions of a task

    NASA Technical Reports Server (NTRS)

    Longman, Richard W.; Chang, Chi-Kuang

    1990-01-01

    In many applications, control systems are asked to perform the same task repeatedly. Learning control laws have been developed over the last few years that allow the controller to improve its performance each repetition, and to converge to zero error in tracking a desired trajectory. This paper generates a new type of learning control law that learns to minimize a quadratic cost function for tracking. Besides being of interest in its own right, this objective alleviates the need to specify a desired trajectory that can actually be performed by the system. The approach used here is to adapt appropriate methods from numerical optimization theory in order to produce learning control algorithms that adjust the system command from repetition to repetition in order to converge to the quadratic cost optimal trajectory.

  19. Motor-response learning at a process control panel by an autonomous robot

    SciTech Connect

    Spelt, P.F.; de Saussure, G.; Lyness, E.; Pin, F.G.; Weisbin, C.R.

    1988-01-01

    The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring and manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.

  20. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    ERIC Educational Resources Information Center

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  1. The Effects of Instructor Control of Online Learning Environments on Satisfaction and Perceived Learning

    ERIC Educational Resources Information Center

    Costley, Jamie; Lange, Christopher

    2016-01-01

    Instructional design is important as it helps set the discourse, context, and content of learning in an online environment. Specific instructional design decisions do not only play a part in the discourse of the learners, but they can affect the learners' levels of satisfaction and perceived learning as well. Numerous studies have shown the value…

  2. Channeled spectropolarimetry using iterative reconstruction

    NASA Astrophysics Data System (ADS)

    Lee, Dennis J.; LaCasse, Charles F.; Craven, Julia M.

    2016-05-01

    Channeled spectropolarimeters (CSP) measure the polarization state of light as a function of wavelength. Conventional Fourier reconstruction suffers from noise, assumes the channels are band-limited, and requires uniformly spaced samples. To address these problems, we propose an iterative reconstruction algorithm. We develop a mathematical model of CSP measurements and minimize a cost function based on this model. We simulate a measured spectrum using example Stokes parameters, from which we compare conventional Fourier reconstruction and iterative reconstruction. Importantly, our iterative approach can reconstruct signals that contain more bandwidth, an advancement over Fourier reconstruction. Our results also show that iterative reconstruction mitigates noise effects, processes non-uniformly spaced samples without interpolation, and more faithfully recovers the ground truth Stokes parameters. This work offers a significant improvement to Fourier reconstruction for channeled spectropolarimetry.

  3. Modeling ITER ECH Waveguide Performance

    NASA Astrophysics Data System (ADS)

    Kaufman, M. C.; Lau, C. H.

    2014-10-01

    There are stringent requirements for mode purity and for on-target power as a percentage of source power for the ECH transmission lines on ITER. The design goal is less than 10% total power loss through the line and 95% HE11 mode at the diamond window. The dominant loss mechanism is mode conversion (MC) into higher order modes, and to maintain mode purity, these losses must be minimized. Miter bends and waveguide curvature are major sources of mode conversion. This work uses a code which calculates the mode conversion and attenuation of an arbitrary set of polarized waveguide modes in circular corrugated waveguide with non-zero axial curvature and miter bends. The transmission line is modeled as a structural beam with deformations due to misalignment of waveguide supports, tilts at the interfaces between waveguide sections, gravitational loading, and the extrusion and fabrication process. As these sources of curvature are statistical in nature, the resulting MC losses are found via Monte Carlo modeling. The results of this analysis will provide design guidance for waveguide support span lengths, requirements for minimum alignment offsets, and requirements for waveguide fabrication and quality control.

  4. Magnetic fusion and project ITER

    SciTech Connect

    Park, H.K.

    1992-01-01

    It has already been demonstrated that our economics and international relationship are impacted by an energy crisis. For the continuing prosperity of the human race, a new and viable energy source must be developed within the next century. It is evident that the cost will be high and will require a long term commitment to achieve this goal due to a high degree of technological and scientific knowledge. Energy from the controlled nuclear fusion is a safe, competitive, and environmentally attractive but has not yet been completely conquered. Magnetic fusion is one of the most difficult technological challenges. In modem magnetic fusion devices, temperatures that are significantly higher than the temperatures of the sun have been achieved routinely and the successful generation of tens of million watts as a result of scientific break-even is expected from the deuterium and tritium experiment within the next few years. For the practical future fusion reactor, we need to develop reactor relevant materials and technologies. The international project called International Thermonuclear Experimental Reactor (ITER)'' will fulfill this need and the success of this project will provide the most attractive long-term energy source for mankind.

  5. Magnetic fusion and project ITER

    SciTech Connect

    Park, H.K.

    1992-09-01

    It has already been demonstrated that our economics and international relationship are impacted by an energy crisis. For the continuing prosperity of the human race, a new and viable energy source must be developed within the next century. It is evident that the cost will be high and will require a long term commitment to achieve this goal due to a high degree of technological and scientific knowledge. Energy from the controlled nuclear fusion is a safe, competitive, and environmentally attractive but has not yet been completely conquered. Magnetic fusion is one of the most difficult technological challenges. In modem magnetic fusion devices, temperatures that are significantly higher than the temperatures of the sun have been achieved routinely and the successful generation of tens of million watts as a result of scientific break-even is expected from the deuterium and tritium experiment within the next few years. For the practical future fusion reactor, we need to develop reactor relevant materials and technologies. The international project called ``International Thermonuclear Experimental Reactor (ITER)`` will fulfill this need and the success of this project will provide the most attractive long-term energy source for mankind.

  6. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    ERIC Educational Resources Information Center

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  7. Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture

    ERIC Educational Resources Information Center

    Butz, Martin V.; Herbort, Oliver; Hoffmann, Joachim

    2007-01-01

    Autonomously developing organisms face several challenges when learning reaching movements. First, motor control is learned unsupervised or self-supervised. Second, knowledge of sensorimotor contingencies is acquired in contexts in which action consequences unfold in time. Third, motor redundancies must be resolved. To solve all 3 of these…

  8. The Efficiency and Efficacy of Equivalence-Based Learning: A Randomized Controlled Trial

    ERIC Educational Resources Information Center

    Zinn, Tracy E.; Newland, M. Christopher; Ritchie, Katie E.

    2015-01-01

    Because it employs an emergent-learning framework, equivalence-based instruction (EBI) is said to be highly efficient, but its presumed benefits must be compared quantitatively with alternative techniques. In a randomized controlled trial, 61 college students attempted to learn 32 pairs of proprietary and generic drug names using computer-based…

  9. Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments

    ERIC Educational Resources Information Center

    Baylor, Amy L.

    2004-01-01

    While there has been a significant amount of research on technical issues regarding the development of agent-based learning environments (e.g., see the special issue of Journal of "Interactive Learning Research," 10(3/4)), there is less information regarding cognitive foundations for these environments. The management of control is a prime issue…

  10. Child Predictors of Learning to Control Variables via Instruction or Self-Discovery

    ERIC Educational Resources Information Center

    Wagensveld, Barbara; Segers, Eliane; Kleemans, Tijs; Verhoeven, Ludo

    2015-01-01

    We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group. Prior to the intervention, cognitive, scientific,…

  11. The Role of Executive Control of Attention and Selective Encoding for Preschoolers' Learning

    ERIC Educational Resources Information Center

    Roderer, Thomas; Krebs, Saskia; Schmid, Corinne; Roebers, Claudia M.

    2012-01-01

    Selectivity in encoding, aspects of attentional control and their contribution to learning performance were explored in a sample of preschoolers. While the children are performing a learning task, their encoding of relevant and attention towards irrelevant information was recorded through an eye-tracking device. Recognition of target items was…

  12. Rational-Emotive Education, Self-Concept, and Locus of Control among Learning-Disabled Students.

    ERIC Educational Resources Information Center

    Omizo, Michael M.; And Others

    1986-01-01

    Investigated effects of a rational-emotive education (REE) program on learning-disabled adolescents' (N=60) self-concept and locus of control. Results suggest that the REE intervention strategy is an effective approach to helping learning-disabled adolescents enhance some aspects of self-concept and develop a more internal locus of control…

  13. The Effects of Variations in Lesson Control and Practice on Learning from Interactive Video.

    ERIC Educational Resources Information Center

    Hannafin, Michael J.; Colamaio, MaryAnne E.

    1987-01-01

    Discussion of the effects of variations in lesson control and practice on the learning of facts, procedures, and problem-solving skills during interactive video instruction focuses on a study of graduates and advanced level undergraduates learning cardiopulmonary resuscitation (CPR). Embedded questioning methods and posttests used are described.…

  14. Virtual Experiments or Worked Examples? How to Learn the Control of Variable Strategy

    ERIC Educational Resources Information Center

    Liu, Shiyu

    2015-01-01

    This research investigates the role of virtual experiments and worked examples in the learning of the control of variable strategy (CVS). Sixty-nine seventh-grade students participated in this study over a span of 6 weeks and were engaged in worked example learning and/or virtual experimentation to study the knowledge and procedures associated…

  15. Is an Intervention Using Computer Software Effective in Literacy Learning? A Randomised Controlled Trial

    ERIC Educational Resources Information Center

    Brooks, G.; Miles, J. N. V.; Torgerson, C. J.; Torgerson, D. J.

    2006-01-01

    Background: Computer software is widely used to support literacy learning. There are few randomised trials to support its effectiveness. Therefore, there is an urgent need to rigorously evaluate computer software that supports literacy learning. Methods: We undertook a pragmatic randomised controlled trial among pupils aged 11-12 within a single…

  16. Easy to learn, hard to suppress: The impact of learned stimulus-outcome associations on subsequent action control.

    PubMed

    van Wouwe, N C; van den Wildenberg, W P M; Ridderinkhof, K R; Claassen, D O; Neimat, J S; Wylie, S A

    2015-12-01

    The inhibition of impulsive response tendencies that conflict with goal-directed action is a key component of executive control. An emerging literature reveals that the proficiency of inhibitory control is modulated by expected or unexpected opportunities to earn reward or avoid punishment. However, less is known about how inhibitory control is impacted by the processing of task-irrelevant stimulus information that has been associated previously with particular outcomes (reward or punishment) or response tendencies (action or inaction). We hypothesized that stimulus features associated with particular action-valence tendencies, even though task irrelevant, would modulate inhibitory control processes. Participants first learned associations between stimulus features (color), actions, and outcomes using an action-valence learning task that orthogonalizes action (action, inaction) and valence (reward, punishment). Next, these stimulus features were embedded in a Simon task as a task-irrelevant stimulus attribute. We analyzed the effects of action-valence associations on the Simon task by means of distributional analysis to reveal the temporal dynamics. Learning patterns replicated previously reported biases; inherent, Pavlovian-like mappings (action-reward, inaction-punishment avoidance) were easier to learn than mappings conflicting with these biases (action-punishment avoidance, inaction-reward). More importantly, results from two experiments demonstrated that the easier to learn, Pavlovian-like action-valence associations interfered with the proficiency of inhibiting impulsive actions in the Simon task. Processing conflicting associations led to more proficient inhibitory control of impulsive actions, similar to Simon trials without any association. Fast impulsive errors were reduced for trials associated with punishment in comparison to reward trials or trials without any valence association. These findings provide insight into the temporal dynamics of task

  17. The ITER project construction status

    NASA Astrophysics Data System (ADS)

    Motojima, O.

    2015-10-01

    The pace of the ITER project in St Paul-lez-Durance, France is accelerating rapidly into its peak construction phase. With the completion of the B2 slab in August 2014, which will support about 400 000 metric tons of the tokamak complex structures and components, the construction is advancing on a daily basis. Magnet, vacuum vessel, cryostat, thermal shield, first wall and divertor structures are under construction or in prototype phase in the ITER member states of China, Europe, India, Japan, Korea, Russia, and the United States. Each of these member states has its own domestic agency (DA) to manage their procurements of components for ITER. Plant systems engineering is being transformed to fully integrate the tokamak and its auxiliary systems in preparation for the assembly and operations phase. CODAC, diagnostics, and the three main heating and current drive systems are also progressing, including the construction of the neutral beam test facility building in Padua, Italy. The conceptual design of the Chinese test blanket module system for ITER has been completed and those of the EU are well under way. Significant progress has been made addressing several outstanding physics issues including disruption load characterization, prediction, avoidance, and mitigation, first wall and divertor shaping, edge pedestal and SOL plasma stability, fuelling and plasma behaviour during confinement transients and W impurity transport. Further development of the ITER Research Plan has included a definition of the required plant configuration for 1st plasma and subsequent phases of ITER operation as well as the major plasma commissioning activities and the needs of the accompanying R&D program to ITER construction by the ITER parties.

  18. Action Control, Motivated Strategies, and Integrative Motivation as Predictors of Language Learning Affect and the Intention to Continue Learning French

    ERIC Educational Resources Information Center

    MacIntyre, Peter D.; Blackie, Rebecca A.

    2012-01-01

    The present study examines the relative ability of variables from three motivational frameworks to predict four non-linguistic outcomes of language learning. The study examines Action Control Theory with its measures of (1) hesitation, (2) volatility and (3) rumination. The study also examined Pintrich's expectancy-value model that uses measures…

  19. Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

    PubMed Central

    Worm, Bjarne Skjødt; Jensen, Kenneth

    2013-01-01

    Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials. PMID:24229729

  20. Influence of self-controlled feedback on learning a serial motor skill.

    PubMed

    Lim, Soowoen; Ali, Asif; Kim, Wonchan; Kim, Jingu; Choi, Sungmook; Radlo, Steven J

    2015-04-01

    Self-controlled feedback on a variety of tasks are well established as effective means of facilitating motor skill learning. This study assessed the effects of self-controlled feedback on the performance of a serial motor skill. The task was to learn the sequence of 18 movements that make up the Taekwondo Poomsae Taegeuk first, which is the first beginner's practice form learned in this martial art. Twenty-four novice female participants (M age=27.2 yr., SD=1.8) were divided into two groups. All participants performed 16 trials in 4 blocks of the acquisition phase and 20 hr. later, 8 trials in 2 blocks of the retention phase. The self-controlled feedback group had significantly higher performance compared to the yoked-feedback group with regard to acquisition and retention. The results of this study may contribute to the literature regarding feedback by extending the usefulness of self-controlled feedback for learning a serial skill.

  1. Frequent Deadlines: Evaluating the Effect of Learner Control on Healthcare Executives' Performance in Online Learning

    ERIC Educational Resources Information Center

    Fulton, Lawrence V.; Ivanitskaya, Lana V.; Bastian, Nathaniel D.; Erofeev, Dmitry A.; Mendez, Francis A.

    2013-01-01

    In a three-group, gender-matched, preexisting knowledge-controlled, randomized experiment, we evaluated the effect of learner control over study pace on healthcare executives' performance in an online statistics course. Overall, frequent deadlines enhanced distribution of practice and improved learning. Students with less control over pace (in…

  2. Comparing the Effects of Self-Controlled and Examiner-Controlled Feedback on Learning in Children With Developmental Coordination Disorder

    PubMed Central

    Zamani, Mohamad Hosein; Fatemi, Rouholah; Soroushmoghadam, Keyvan

    2015-01-01

    Background: Feedback can improve task learning in children with developmental coordination disorder (DCD). However, the frequency and type of feedback may play different role in learning and needs to more investigations. Objectives: The aim of this study was to evaluate the acquisition and retention of new feedback skills in children with DCD under different frequency of self-control and control examiner feedback. Materials and Methods: In this quasi-experimental study with pretest-posttest design, participants based on their retention were divided into four feedback groups: self-controlled feedback groups with frequencies of 50% and75%, experimenter controls with frequencies of 50% and 75%. The study sample consisted of 24 boys with DCD aged between 9 to 11 years old in Ahvaz City, Iran. Then subjects practiced 30 throwing (6 blocks of 5 attempts) in eighth session. Acquisition test immediately after the last training session, and then the retention test were taken. Data were analyzed using the paired t-test, ANOVA and Tukey tests. Results: The results showed no significant difference between groups in the acquisition phase (P > 0.05). However,in the retention session, group of self-control showed better performance than the control tester group (P < 0.05). Conclusions: Based on the current findings, self-control feedback with high frequency leads to more learning in DCD children. The results of this study can be used in rehabilitation programs to improve performance and learning in children with DCD. PMID:26834805

  3. Finite-approximation-error-based discrete-time iterative adaptive dynamic programming.

    PubMed

    Wei, Qinglai; Wang, Fei-Yue; Liu, Derong; Yang, Xiong

    2014-12-01

    In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The generalized value iteration algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance index function in each iteration cannot accurately be obtained, for the first time a new "design method of the convergence criteria" for the finite-approximation-error-based generalized value iteration algorithm is established. A suitable approximation error can be designed adaptively to make the iterative performance index function converge to a finite neighborhood of the optimal performance index function. Neural networks are used to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the developed method. PMID:25265640

  4. A Real-time Reinforcement Learning Control System with H∞ Tracking Performance Compensator

    NASA Astrophysics Data System (ADS)

    Uchiyama, Shogo; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

    Robust control theory generally guarantees robustness and stability of the closed-loop system. It however requires a mathematical model of the system to design the control system. It therefore can't often deal with nonlinear systems due to difficulty of modeling of the system. On the other hand, reinforcement learning methods can deal with nonlinear systems without any mathematical model. It however usually doesn't guarantee the stability of the system control. In this paper, we propose a “Real-time Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear systems and robust control theory to guarantee the robustness and stability of the system. Moreover, we analyze the stability of the proposed system using H∞ tracking performance and Lyapunov function. Finally, through the computer simulation for controlling an inverted pendulum system, we show the effectiveness of the proposed method.

  5. Applying principles of motor learning and control to upper extremity rehabilitation

    PubMed Central

    Muratori, Lisa M.; Lamberg, Eric M.; Quinn, Lori; Duff, Susan V.

    2013-01-01

    The purpose of this article is to provide a brief review of the principles of motor control and learning. Different models of motor control from historical to contemporary are presented with emphasis on the systems model. Concepts of motor learning including skill acquisition, measurement of learning, and methods to promote skill acquisition by examining the many facets of practice scheduling and use of feedback are provided. A fictional client case is introduced and threaded throughout the article to facilitate understanding of these concepts and how they can be applied to clinical practice. PMID:23598082

  6. Towards unconventional computing through simulated evolution: control of nonlinear media by a learning classifier system.

    PubMed

    Bull, Larry; Budd, Adam; Stone, Christopher; Uroukov, Ivan; de Lacy Costello, Ben; Adamatzky, Andrew

    2008-01-01

    We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.

  7. Experimental Vertical Stability Studies for ITER Performance and Design Guidance

    SciTech Connect

    Humphreys, D A; Casper, T A; Eidietis, N; Ferrera, M; Gates, D A; Hutchinson, I H; Jackson, G L; Kolemen, E; Leuer, J A; Lister, J; LoDestro, L L; Meyer, W H; Pearlstein, L D; Sartori, F; Walker, M L; Welander, A S; Wolfe, S M

    2008-10-13

    Operating experimental devices have provided key inputs to the design process for ITER axisymmetric control. In particular, experiments have quantified controllability and robustness requirements in the presence of realistic noise and disturbance environments, which are difficult or impossible to characterize with modeling and simulation alone. This kind of information is particularly critical for ITER vertical control, which poses some of the highest demands on poloidal field system performance, since the consequences of loss of vertical control can be very severe. The present work describes results of multi-machine studies performed under a joint ITPA experiment on fundamental vertical control performance and controllability limits. We present experimental results from Alcator C-Mod, DIII-D, NSTX, TCV, and JET, along with analysis of these data to provide vertical control performance guidance to ITER. Useful metrics to quantify this control performance include the stability margin and maximum controllable vertical displacement. Theoretical analysis of the maximum controllable vertical displacement suggests effective approaches to improving performance in terms of this metric, with implications for ITER design modifications. Typical levels of noise in the vertical position measurement which can challenge the vertical control loop are assessed and analyzed.

  8. A Control Systems Concept Inventory Test Design and Assessment

    ERIC Educational Resources Information Center

    Bristow, M.; Erkorkmaz, K.; Huissoon, J. P.; Jeon, Soo; Owen, W. S.; Waslander, S. L.; Stubley, G. D.

    2012-01-01

    Any meaningful initiative to improve the teaching and learning in introductory control systems courses needs a clear test of student conceptual understanding to determine the effectiveness of proposed methods and activities. The authors propose a control systems concept inventory. Development of the inventory was collaborative and iterative. The…

  9. Using machine learning to blend human and robot controls for assisted wheelchair navigation.

    PubMed

    Goil, Aditya; Derry, Matthew; Argall, Brenna D

    2013-06-01

    This work presents an algorithm for collaborative control of an assistive semi-autonomous wheelchair. Our approach is based on a statistical machine learning technique to learn task variability from demonstration examples. The algorithm has been developed in the context of shared-control powered wheelchairs that provide assistance to individuals with impairments that affect their control in challenging driving scenarios, like doorway navigation. We validate our algorithm within a simulation environment, and find that with relatively few demonstrations, our approach allows for safe traversal of the doorway while maintaining a high level of user control.

  10. ITER Construction--Plant System Integration

    SciTech Connect

    Tada, E.; Matsuda, S.

    2009-02-19

    This brief paper introduces how the ITER will be built in the international collaboration. The ITER Organization plays a central role in constructing ITER and leading it into operation. Since most of the ITER components are to be provided in-kind from the member countries, integral project management should be scoped in advance of real work. Those include design, procurement, system assembly, testing, licensing and commissioning of ITER.

  11. Learning from adaptive neural dynamic surface control of strict-feedback systems.

    PubMed

    Wang, Min; Wang, Cong

    2015-06-01

    Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

  12. ITER project and fusion technology

    NASA Astrophysics Data System (ADS)

    Takatsu, H.

    2011-09-01

    In the sessions of ITR, FTP and SEE of the 23rd IAEA Fusion Energy Conference, 159 papers were presented in total, highlighted by the remarkable progress of the ITER project: ITER baseline has been established and procurement activities have been started as planned with a target of realizing the first plasma in 2019; ITER physics basis is sound and operation scenarios and operational issues have been extensively studied in close collaboration with the worldwide physics community; the test blanket module programme has been incorporated into the ITER programme and extensive R&D works are ongoing in the member countries with a view to delivering their own modules in a timely manner according to the ITER master schedule. Good progress was also reported in the areas of a variety of complementary activities to DEMO, including Broader Approach activities and long-term technology. This paper summarizes the highlights of the papers presented in the ITR, FTP and SEE sessions with a minimum set of background information.

  13. Understanding Self-Controlled Motor Learning Protocols through the Self-Determination Theory.

    PubMed

    Sanli, Elizabeth A; Patterson, Jae T; Bray, Steven R; Lee, Timothy D

    2012-01-01

    The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

  14. Language experience differentiates prefrontal and subcortical activation of the cognitive control network in novel word learning.

    PubMed

    Bradley, Kailyn A L; King, Kelly E; Hernandez, Arturo E

    2013-02-15

    The purpose of this study was to examine the cognitive control mechanisms in adult English speaking monolinguals compared to early sequential Spanish-English bilinguals during the initial stages of novel word learning. Functional magnetic resonance imaging during a lexico-semantic task after only 2h of exposure to novel German vocabulary flashcards showed that monolinguals activated a broader set of cortical control regions associated with higher-level cognitive processes, including the supplementary motor area (SMA), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC), as well as the caudate, implicated in cognitive control of language. However, bilinguals recruited a more localized subcortical network that included the putamen, associated more with motor control of language. These results suggest that experience managing multiple languages may differentiate the learning strategy and subsequent neural mechanisms of cognitive control used by bilinguals compared to monolinguals in the early stages of novel word learning.

  15. Effects of a National Public Service Information Campaign on Crime Prevention: Perspectives from Social Learning and Social Control Theory.

    ERIC Educational Resources Information Center

    Lordan, Edward J.; Kwon, Joongrok

    This study examined the effects of public service advertising from two theoretical backgrounds: social learning theory and social control theory. Traditional social learning theory assumes that learning occurs by subjects performing responses and experiencing their effects, with reinforcement as the main determinant. Social control theory, as…

  16. Automatic learning rate adjustment for self-supervising autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  17. DNA methylation-mediated control of learning and memory.

    PubMed

    Yu, Nam-Kyung; Baek, Sung Hee; Kaang, Bong-Kiun

    2011-01-19

    Animals constantly receive and respond to external or internal stimuli, and these experiences are learned and memorized in their brains. In animals, this is a crucial feature for survival, by making it possible for them to adapt their behavioral patterns to the ever-changing environment. For this learning and memory process, nerve cells in the brain undergo enormous molecular and cellular changes, not only in the input-output-related local subcellular compartments but also in the central nucleus. Interestingly, the DNA methylation pattern, which is normally stable in a terminally differentiated cell and defines the cell type identity, is emerging as an important regulatory mechanism of behavioral plasticity. The elucidation of how this covalent modification of DNA, which is known to be the most stable epigenetic mark, contributes to the complex orchestration of animal behavior is a fascinating new research area. We will overview the current understanding of the mechanism of modifying the methyl code on DNA and its impact on learning and memory.

  18. Construction Safety Forecast for ITER

    SciTech Connect

    cadwallader, lee charles

    2006-11-01

    The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.

  19. Error Field Correction in ITER

    SciTech Connect

    Park, Jong-kyu; Boozer, Allen H.; Menard, Jonathan E.; Schaffer, Michael J.

    2008-05-22

    A new method for correcting magnetic field errors in the ITER tokamak is developed using the Ideal Perturbed Equilibrium Code (IPEC). The dominant external magnetic field for driving islands is shown to be localized to the outboard midplane for three ITER equilibria that represent the projected range of operational scenarios. The coupling matrices between the poloidal harmonics of the external magnetic perturbations and the resonant fields on the rational surfaces that drive islands are combined for different equilibria and used to determine an ordered list of the dominant errors in the external magnetic field. It is found that efficient and robust error field correction is possible with a fixed setting of the correction currents relative to the currents in the main coils across the range of ITER operating scenarios that was considered.

  20. US--ITER activation analysis

    SciTech Connect

    Attaya, H.; Gohar, Y.; Smith, D.

    1990-09-01

    Activation analysis has been made for the US ITER design. The radioactivity and the decay heat have been calculated, during operation and after shutdown for the two ITER phases, the Physics Phase and the Technology Phase. The Physics Phase operates about 24 full power days (FPDs) at fusion power level of 1100 MW and the Technology Phase has 860 MW fusion power and operates for about 1360 FPDs. The point-wise gamma sources have been calculated everywhere in the reactor at several times after shutdown of the two phases and are then used to calculate the biological dose everywhere in the reactor. Activation calculations have been made also for ITER divertor. The results are presented for different continuous operation times and for only one pulse. The effect of the pulsed operation on the radioactivity is analyzed. 6 refs., 12 figs., 1 tab.

  1. Episodic Contributions to Sequential Control: Learning from a Typist's Touch

    ERIC Educational Resources Information Center

    Crump, Matthew J. C.; Logan, Gordon D.

    2010-01-01

    Sequential control over routine action is widely assumed to be controlled by stable, highly practiced representations. Our findings demonstrate that the processes controlling routine actions in the domain of skilled typing can be flexibly manipulated by memory processes coding recent experience with typing particular words and letters. In two…

  2. Iterated binomial sums and their associated iterated integrals

    NASA Astrophysics Data System (ADS)

    Ablinger, J.; Blümlein, J.; Raab, C. G.; Schneider, C.

    2014-11-01

    We consider finite iterated generalized harmonic sums weighted by the binomial binom{2k}{k} in numerators and denominators. A large class of these functions emerges in the calculation of massive Feynman diagrams with local operator insertions starting at 3-loop order in the coupling constant and extends the classes of the nested harmonic, generalized harmonic, and cyclotomic sums. The binomially weighted sums are associated by the Mellin transform to iterated integrals over square-root valued alphabets. The values of the sums for N → ∞ and the iterated integrals at x = 1 lead to new constants, extending the set of special numbers given by the multiple zeta values, the cyclotomic zeta values and special constants which emerge in the limit N → ∞ of generalized harmonic sums. We develop algorithms to obtain the Mellin representations of these sums in a systematic way. They are of importance for the derivation of the asymptotic expansion of these sums and their analytic continuation to N in {C}. The associated convolution relations are derived for real parameters and can therefore be used in a wider context, as, e.g., for multi-scale processes. We also derive algorithms to transform iterated integrals over root-valued alphabets into binomial sums. Using generating functions we study a few aspects of infinite (inverse) binomial sums.

  3. Adventitious Reinforcement of Maladaptive Stimulus Control Interferes with Learning.

    PubMed

    Saunders, Kathryn J; Hine, Kathleen; Hayashi, Yusuke; Williams, Dean C

    2016-09-01

    Persistent error patterns sometimes develop when teaching new discriminations. These patterns can be adventitiously reinforced, especially during long periods of chance-level responding (including baseline). Such behaviors can interfere with learning a new discrimination. They can also disrupt already learned discriminations, if they re-emerge during teaching procedures that generate errors. We present an example of this process. Our goal was to teach a boy with intellectual disabilities to touch one of two shapes on a computer screen (in technical terms, a simple simultaneous discrimination). We used a size-fading procedure. The correct stimulus was at full size, and the incorrect-stimulus size increased in increments of 10 %. Performance was nearly error free up to and including 60 % of full size. In a probe session with the incorrect stimulus at full size, however, accuracy plummeted. Also, a pattern of switching between choices, which apparently had been established in classroom instruction, re-emerged. The switching pattern interfered with already-learned discriminations. Despite having previously mastered a fading step with the incorrect stimulus up to 60 %, we were unable to maintain consistently high accuracy beyond 20 % of full size. We refined the teaching program such that fading was done in smaller steps (5 %), and decisions to "step back" to a smaller incorrect stimulus were made after every 5-instead of 20-trials. Errors were rare, switching behavior stopped, and he mastered the discrimination. This is a practical example of the importance of designing instruction that prevents adventitious reinforcement of maladaptive discriminated response patterns by reducing errors during acquisition. PMID:27622128

  4. Robust reinforcement learning control using integral quadratic constraints for recurrent neural networks.

    PubMed

    Anderson, Charles W; Young, Peter Michael; Buehner, Michael R; Knight, James N; Bush, Keith A; Hittle, Douglas C

    2007-07-01

    The applicability of machine learning techniques for feedback control systems is limited by a lack of stability guarantees. Robust control theory offers a framework for analyzing the stability of feedback control loops, but for the integral quadratic constraint (IQC) framework used here, all components are required to be represented as linear, time-invariant systems plus uncertainties with, for IQCs used here, bounded gain. In this paper, the stability of a control loop including a recurrent neural network (NN) is analyzed by replacing the nonlinear and time-varying components of the NN with IQCs on their gain. As a result, a range of the NN's weights is found within which stability is guaranteed. An algorithm is demonstrated for training the recurrent NN using reinforcement learning and guaranteeing stability while learning.

  5. Maternal Self-Efficacy: Illusory Control and Its Effect on Susceptibility to Learned Helplessness.

    ERIC Educational Resources Information Center

    Donovan, Wilberta L.; And Others

    1990-01-01

    Mothers who exhibited high illusory control showed increased susceptibility to learned helplessness when they tried to solve a child-care task. The early attributional and behavioral style characteristic of high illusory control may be a precursor to overcontrolling, interfering behaviors during the toddler years. (RH)

  6. Controlled Experiment Replication in Evaluation of E-Learning System's Educational Influence

    ERIC Educational Resources Information Center

    Grubisic, Ani; Stankov, Slavomir; Rosic, Marko; Zitko, Branko

    2009-01-01

    We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system's advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal…

  7. Control and Constraint in E-Learning: Choosing When to Choose

    ERIC Educational Resources Information Center

    Dron, Jon

    2007-01-01

    Every learner is on a trajectory, an individual path that involves choices about what to do next in order to learn, choices that are bounded by intrinsic and extrinsic constraints. In some cases the learner controls those choices, sometimes they are made by someone or something else, sometimes control is negotiated, or it emerges from complex…

  8. Effects of Social Reinforcement, Locus of Control, and Cognitive Style on Concept Learning among Retarded Children.

    ERIC Educational Resources Information Center

    Panda, Kailas C.

    To examine the effects of locus of control (the extent to which an individual feels he has control over his own behavior) and cognitive style variables on learning deficits among mentally handicapped children, 80 mentally retarded boys (IQ 50 to 83, age 160 to 196 months) were administered a battery of tests. Analyses of student performance…

  9. Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

    ERIC Educational Resources Information Center

    Mendez, J. A.; Gonzalez, E. J.

    2011-01-01

    This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The…

  10. Children with a Learning Disorder Show Prospective Control Impairments during Visuomanual Tracking

    ERIC Educational Resources Information Center

    van Roon, Dominique; Caeyenberghs, Karen; Swinnen, Stephan P.; Smits-Engelsman, Bouwien C. M.

    2010-01-01

    To examine whether children with a learning disorder (LD) are able to use prospective motor control, 30 children with LD (mean age 8 years and 11 months) and an age- and gender-matched control group were asked to smoothly track an accelerating dot presented on a monitor by moving an electronic pen on a digitizer. Children with LD performed worse…

  11. Lessons Learned from the Node 1 Temperature and Humidity Control Subsystem Design

    NASA Technical Reports Server (NTRS)

    Williams, David E.

    2010-01-01

    Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Temperature and Humidity Control (THC) subsystem and it will document some of the lessons that have been learned to date for this subsystem and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs. 1

  12. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    PubMed

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  13. Delayed Over-Relaxation for iterative methods

    NASA Astrophysics Data System (ADS)

    Antuono, M.; Colicchio, G.

    2016-09-01

    We propose a variant of the relaxation step used in the most widespread iterative methods (e.g. Jacobi Over-Relaxation, Successive Over-Relaxation) which combines the iteration at the predicted step, namely (n + 1), with the iteration at step (n - 1). We provide a theoretical analysis of the proposed algorithm by applying such a delayed relaxation step to a generic (convergent) iterative scheme. We prove that, under proper assumptions, this significantly improves the convergence rate of the initial iterative method. As a relevant example, we apply the proposed algorithm to the solution of the Poisson equation, highlighting the advantages in comparison with classical iterative models.

  14. ODE System Solver W. Krylov Iteration & Rootfinding

    1991-09-09

    LSODKR is a new initial value ODE solver for stiff and nonstiff systems. It is a variant of the LSODPK and LSODE solvers, intended mainly for large stiff systems. The main differences between LSODKR and LSODE are the following: (a) for stiff systems, LSODKR uses a corrector iteration composed of Newton iteration and one of four preconditioned Krylov subspace iteration methods. The user must supply routines for the preconditioning operations, (b) Within the corrector iteration,more » LSODKR does automatic switching between functional (fixpoint) iteration and modified Newton iteration, (c) LSODKR includes the ability to find roots of given functions of the solution during the integration.« less

  15. Learning guide for the terminal configured vehicle advanced guidance and control system mode select panel

    NASA Technical Reports Server (NTRS)

    Anderson, M. A.; Callahan, R.

    1981-01-01

    This learning guide is designed to assist pilots in taking the PLATO presimulator training course on the advanced guidance and control system mode select panel. The learning guide is divided into five sections. The first section, the introduction, presents the course goals, prerequisites, definition of PLATO activities, and a suggested approach to completing the course. The remaining four sections present the purpose, learning activities and summary of each lesson of the AGCS PLATO course, which consists of (1) AGCS introduction; (2) lower order modes; (3) higher order modes; and (4) an arrival route exercise.

  16. Continued Fractions and Iterative Processes.

    ERIC Educational Resources Information Center

    Bevis, Jean H.; Boal, Jan L.

    1982-01-01

    Continued fractions and associated sequences are viewed to constitute a rich area of study for mathematics students, by supporting instruction on algebraic and computational skills, mathematical induction, convergence of sequences, and interpretation of function graphs. An iterative method of approximating square roots opens suggestions for…

  17. Networking Theories by Iterative Unpacking

    ERIC Educational Resources Information Center

    Koichu, Boris

    2014-01-01

    An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…

  18. Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems.

    PubMed

    Wang, Cong; Wang, Min; Liu, Tengfei; Hill, David J

    2012-10-01

    This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

  19. Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

    PubMed

    Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

    2012-08-01

    An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures.

  20. Design issues for a reinforcement-based self-learning fuzzy controller

    NASA Technical Reports Server (NTRS)

    Yen, John; Wang, Haojin; Dauherity, Walter

    1993-01-01

    Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

  1. Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms.

    PubMed

    Iscen, Atil; Caluwaerts, Ken; Bruce, Jonathan; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan

    2015-01-01

    Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem. Instead of creating controls to handle the intricate dynamics of these robots, we can simply evolve the controls using a simulation to provide an evaluation function. In this article, we show how such a control paradigm can be applied to an emerging field within soft robotics: robots based on tensegrity structures. We take the model of the Spherical Underactuated Planetary Exploration Robot ball (SUPERball), an icosahedron tensegrity robot under production at NASA Ames Research Center, develop a rolling locomotion algorithm, and study the learned behavior using an accurate model of the SUPERball simulated in the NASA Tensegrity Robotics Toolkit. We first present the historical-average fitness-shaping algorithm for coevolutionary algorithms to speed up learning while favoring robustness over optimality. Second, we use a distributed control approach by coevolving open-loop control signals for each controller. Being simple and distributed, open-loop controllers can be readily implemented on SUPERball hardware without the need for sensor information or precise coordination. We analyze signals of different complexities and frequencies. Among the learned policies, we take one of the best and use it to analyze different aspects of the rolling gait, such as lengths, tensions, and energy consumption. We also discuss the correlation between the signals controlling different parts of the tensegrity robot. PMID:25951199

  2. Active beam spectroscopy for ITER

    NASA Astrophysics Data System (ADS)

    von Hellermann, M. G.; Barnsley, R.; Biel, W.; Delabie, E.; Hawkes, N.; Jaspers, R.; Johnson, D.; Klinkhamer, F.; Lischtschenko, O.; Marchuk, O.; Schunke, B.; Singh, M. J.; Snijders, B.; Summers, H. P.; Thomas, D.; Tugarinov, S.; Vasu, P.

    2010-11-01

    Since the first feasibility studies of active beam spectroscopy on ITER in 1995 the proposed diagnostic has developed into a well advanced and mature system. Substantial progress has been achieved on the physics side including comprehensive performance studies based on an advanced predictive code, which simulates active and passive features of the expected spectral ranges. The simulation has enabled detailed specifications for an optimized instrumentation and has helped to specify suitable diagnostic neutral beam parameters. Four ITER partners share presently the task of developing a suite of ITER active beam diagnostics, which make use of the two 0.5 MeV/amu 18 MW heating neutral beams and a dedicated 0.1 MeV/amu, 3.6 MW diagnostic neutral beam. The IN ITER team is responsible for the DNB development and also for beam physics related aspects of the diagnostic. The RF will be responsible for edge CXRS system covering the outer region of the plasma (1> r/ a>0.4) using an equatorial observation port, and the EU will develop the core CXRS system for the very core (0< r/ a<0.7) using a top observation port. Thus optimum radial resolution is ensured for each system with better than a/30 resolution. Finally, the US will develop a dedicated MSE system making use of the HNBs and two equatorial ports. With appropriate modification, these systems could also potentially provide information on alpha particle slowing-down features. . On the engineering side, comprehensive preparations were made involving the development of an observation periscope, a neutron labyrinth optical system and design studies for remote maintenance including the exchange of the first mirror assembly, a critical issue for the operation of the CXRS diagnostic in the harsh ITER environment. Additionally, an essential change of the orientation of the DNB injection angle and specification of suitable blanket aperture has been made to avoid trapped particle damage to the first wall.

  3. Learning-based controller for biotechnology processing, and method of using

    DOEpatents

    Johnson, John A.; Stoner, Daphne L.; Larsen, Eric D.; Miller, Karen S.; Tolle, Charles R.

    2004-09-14

    The present invention relates to process control where some of the controllable parameters are difficult or impossible to characterize. The present invention relates to process control in biotechnology of such systems, but not limited to. Additionally, the present invention relates to process control in biotechnology minerals processing. In the inventive method, an application of the present invention manipulates a minerals bioprocess to find local exterma (maxima or minima) for selected output variables/process goals by using a learning-based controller for bioprocess oxidation of minerals during hydrometallurgical processing. The learning-based controller operates with or without human supervision and works to find processor optima without previously defined optima due to the non-characterized nature of the process being manipulated.

  4. DIII-D Research in Support of ITER

    SciTech Connect

    Strait, E

    2008-10-14

    DIII-D research is providing key information for the design and operation of ITER. Discharges that simulate ITER operating scenarios in conventional H-mode, advanced inductive, hybrid, and steady state regimes have achieved normalized performance consistent with ITER's goals for fusion performance. Stationary discharges with high {beta}{sub N} and 90% noninductive current that project to Q=5 in ITER have been sustained for a current relaxation time ({approx}2.5 s), and high-beta wall-stabilized discharges with fully non-inductive current drive have been sustained for more than one second. Detailed issues of plasma control have been addressed, including the development of a new large-bore startup scenario for ITER. A broad research program provides the physics basis for predicting the performance of ITER. Recent key results include the discovery that the L-H power threshold is reduced with low neutral beam torque, and the development of a successful model for prediction of the H-mode pedestal height in DIII-D. Research areas with the potential to improve ITER's performance include the demonstration of ELM-free 'QH-mode' discharges with both co and counter-injection, and validation of the predicted torque generated by static, non-axisymmetric magnetic fields. New diagnostics provide detailed benchmarking of turbulent transport codes and direct measurements of the anomalous transport of fast ions by Alfven instabilities. DIII-D research also contributes to the basis for reliable operation in ITER, through active control of the chief performance-limiting instabilities. Recently, ELM suppression with resonant magnetic perturbations has been demonstrated at collisionality similar to ITER's, while simultaneous stabilization of NTMs (by localized current drive) and RWMs (by magnetic feedback) has allowed stable operation at high beta and low rotation. In research aimed at improving the lifetime of material surfaces near the plasma, recent experiments have investigated

  5. Microeconomics of yield learning and process control in semiconductor manufacturing

    NASA Astrophysics Data System (ADS)

    Monahan, Kevin M.

    2003-06-01

    Simple microeconomic models that directly link yield learning to profitability in semiconductor manufacturing have been rare or non-existent. In this work, we review such a model and provide links to inspection capability and cost. Using a small number of input parameters, we explain current yield management practices in 200mm factories. The model is then used to extrapolate requirements for 300mm factories, including the impact of technology transitions to 130nm design rules and below. We show that the dramatic increase in value per wafer at the 300mm transition becomes a driver for increasing metrology and inspection capability and sampling. These analyses correlate well wtih actual factory data and often identify millions of dollars in potential cost savings. We demonstrate this using the example of grating-based overlay metrology for the 65nm node.

  6. Lessons learned from benchless laser applied to laser vibration control

    NASA Astrophysics Data System (ADS)

    Henderson, D. A.

    1984-01-01

    Design criteria and concepts for 'benchless' airborne high-energy-laser optics are discussed in a review of US Air Force efforts since about 1970. The original benchless-laser design (Lloyd et al., 1977 and 1979) uses one fixed and one actively controlled six-degree-of-freedom mirror, and the Vicon (vibration control) concept (Katz and Kalejs, 1983) incorporates beam-type rotational sensors, lateral-array type translational sensors, and ball-swivel cooling-system joints. The current laser-vibrational-control (Lavicon) program is characterized, considering recent improvements in sensors, actuators, structural design, and disturbance-minimization techniques. Development of a computer model based on a finite-element technique to obtain eigenvalues, modes, control errors, line-of-sight errors, and beam quality is urged.

  7. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

    PubMed

    Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

    2011-01-01

    As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. PMID:22275543

  8. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2006-01-01

    A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

  9. Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.

    PubMed

    Mason, D G; Ross, J J; Edwards, N D; Linkens, D A; Reilly, C S

    1999-06-01

    Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion. PMID:10356301

  10. A Hebbian feedback covariance learning paradigm for self-tuning optimal control.

    PubMed

    Young, D L; Poon, C S

    2001-01-01

    We propose a novel adaptive optimal control paradigm inspired by Hebbian covariance synaptic adaptation, a preeminent model of learning and memory as well as other malleable functions in the brain. The adaptation is driven by the spontaneous fluctuations in the system input and output, the covariance of which provides useful information about the changes in the system behavior. The control structure represents a novel form of associative reinforcement learning in which the reinforcement signal is implicitly given by the covariance of the input-output (I/O) signals. Theoretical foundations for the paradigm are derived using Lyapunov theory and are verified by means of computer simulations. The learning algorithm is applicable to a general class of nonlinear adaptive control problems. This on-line direct adaptive control method benefits from a computationally straightforward design, proof of convergence, no need for complete system identification, robustness to noise and uncertainties, and the ability to optimize a general performance criterion in terms of system states and control signals. These attractive properties of Hebbian feedback covariance learning control lend themselves to future investigations into the computational functions of synaptic plasticity in biological neurons.

  11. Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

    NASA Technical Reports Server (NTRS)

    Shewhart, Mark

    1991-01-01

    Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

  12. Electrifying the motor engram: effects of tDCS on motor learning and control

    PubMed Central

    de Xivry, Jean-Jacques Orban; Shadmehr, Reza

    2014-01-01

    Learning to control our movements accompanies neuroplasticity of motor areas of the brain. The mechanisms of neuroplasticity are diverse and produce what is referred to as the motor engram, i.e. the neural trace of the motor memory. Transcranial direct current stimulation (tDCS) alters the neural and behavioral correlates of motor learning, but its precise influence on the motor engram is unknown. In this review, we summarize the effects of tDCS on neural activity and suggest a few key principles: 1) firing rates are increased by anodal polarization and decreased by cathodal polarization, 2) anodal polarization strengthens newly formed associations, and 3) polarization modulates the memory of new/preferred firing patterns. With these principles in mind, we review the effects of tDCS on motor control, motor learning, and clinical applications. The increased spontaneous and evoked firing rates may account for the modulation of dexterity in non-learning tasks by tDCS. The facilitation of new association may account for the effect of tDCS on learning in sequence tasks while the ability of tDCS to strengthen memories of new firing patterns may underlie the effect of tDCS on consolidation of skills. We then describe the mechanisms of neuroplasticity of motor cortical areas and how they might be influenced by tDCS. We end with current challenges for the fields of brain stimulation and motor learning. PMID:25200178

  13. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs. PMID:26830003

  14. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

  15. A statistical learning strategy for closed-loop control of fluid flows

    NASA Astrophysics Data System (ADS)

    Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

    2016-04-01

    This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

  16. Learning.

    ERIC Educational Resources Information Center

    Glaser, Robert

    A report on learning psychology and its relationship to the study of school learning emphasizes the increasing interaction between theorists and educational practitioners, particularly in attempting to learn which variables influence the instructional process and to find an appropriate methodology to measure and evaluate learning. "Learning…

  17. A randomized controlled trial on errorless learning in goal management training: study rationale and protocol

    PubMed Central

    2013-01-01

    Background Many brain-injured patients referred for outpatient rehabilitation have executive deficits, notably difficulties with planning, problem-solving and goal directed behaviour. Goal Management Training (GMT) has proven to be an efficacious cognitive treatment for these problems. GMT entails learning and applying an algorithm, in which daily tasks are subdivided into multiple steps. Main aim of the present study is to examine whether using an errorless learning approach (preventing the occurrence of errors during the acquisition phase of learning) contributes to the efficacy of Goal Management Training in the performance of complex daily tasks. Methods/Design The study is a double blind randomized controlled trial, in which the efficacy of Goal Management Training with an errorless learning approach will be compared with conventional Goal Management Training, based on trial and error learning. In both conditions 32 patients with acquired brain injury of mixed etiology will be examined. Main outcome measure will be the performance on two individually chosen everyday-tasks before and after treatment, using a standardized observation scale and goal attainment scaling. Discussion This is the first study that introduces errorless learning in Goal Management Training. It is expected that the GMT-errorless learning approach will improve the execution of complex daily tasks in brain-injured patients with executive deficits. The study can contribute to a better treatment of executive deficits in cognitive rehabilitation. Trial registration (Dutch Trial Register): http://NTR3567 PMID:23786651

  18. Space Stirling Cryocooler Contamination Lessons Learned and Recommended Control Procedures

    NASA Astrophysics Data System (ADS)

    Glaister, D. S.; Price, K.; Gully, W.; Castles, S.; Reilly, J.

    The most important characteristic of a space cryocooler is its reliability over a lifetime typically in excess of 7 years. While design improvements have reduced the probability of mechanical failure, the risk of internal contamination is still significant and has not been addressed in a consistent approach across the industry. A significant fraction of the endurance test and flight units have experienced some performance degradation related to internal contamination. The purpose of this paper is to describe and assess the contamination issues inside long life, space cryocoolers and to recommend procedures to minimize the probability of encountering contamination related failures and degradation. The paper covers the sources of contamination, the degradation and failure mechanisms, the theoretical and observed cryocooler sensitivity, and the recommended prevention procedures and their impact. We begin with a discussion of the contamination sources, both artificial and intrinsic. Next, the degradation and failure mechanisms are discussed in an attempt to arrive at a contaminant susceptibility, from which we can derive a contamination budget for the machine. This theoretical sensitivity is then compared with the observed sensitivity to illustrate the conservative nature of the assumed scenarios. A number of lessons learned on Raytheon, Ball, Air Force Research Laboratory, and NASA GSFC programs are shared to convey the practical aspects of the contamination problem. Then, the materials and processes required to meet the proposed budget are outlined. An attempt is made to present a survey of processes across industry.

  19. Neuromotor Issues in the Learning and Control of Golf Skill

    ERIC Educational Resources Information Center

    Knight, Christopher A.

    2004-01-01

    Theoretical and practical issues related to the neuromotor control of a golf swing are presented in this paper. The typical strategy for golf training consists of high volume repetition with an emphasis on a large variety of isolated swing characteristics. The student is frequently instructed to maintain consistent performance in each swing with…

  20. Applications of Learning Theory: A Course in Self-Control.

    ERIC Educational Resources Information Center

    Kempel, Ted; Collins, Gordon

    A 10-week seminar in self-control for 25 selected junior and senior psychology majors at the College of Wooster is described. The seminar was developed in response to increasing popularity of behavior modification and an interest in exploring applications of a science of behavior. The didactic aspect of the course was complemented by an applied…

  1. Results of Iterative Standards-Setting Procedures for a Performance-Based System for Renewable Certification.

    ERIC Educational Resources Information Center

    Lofton, Glenda G.; And Others

    This report presents the results of an initial, iterative performance standards-setting (SS) task of a comprehensive on-the-job statewide teacher assessment system--the System for Teaching and Learning Assessment and Review (STAR). The 1990-91 STAR assesses and makes inferences about the quality of teaching and learning on sets of assessment…

  2. Research on JET in view of ITER

    NASA Astrophysics Data System (ADS)

    Pamela, Jerome; Ongena, Jef; Watkins, Michael

    2004-11-01

    Research on JET is focused on further development of the two ITER reference plasma scenarios. The ELMy H-Mode, has been extended to lower rho* at high and q_95=3, with simultaneously H_98=0.9, and f_GW=0.9 at I_p=3.5 MA. The dependence of confinement on beta and rho* has been found to be more favorable than given by the IPB98(y,2) scaling. Highlights in the development of Advanced Regimes with Internal Transport Barriers (ITB) and strong reversed shear (q_0=2-3, q_min=1.5-2.5) are : (i) operation at a core density close to the Greenwald limit and (ii) full current drive in 3T/1.8MA ITB plasmas extended to 20 seconds with a JET record injected energy of E≈ 330MJ; (iii) 7 keV Te≈ Ti ITB plasmas at low toroidal rotation, and (iv) wide radius ITB's (r/a=0.6). Furthermore, emphasis in JET is placed on (i) mitigating the impact of ELMs, (ii) understanding the phenomena leading to tritium retention and (iii) preparing burning plasma physics. Recent developments on JET in view of ITER are : (i) real-time control in both ELMy H-Mode and ITB plasmas and (ii) an upgrade of JET with: (a) increased NBI power (b) a new ELM-resilient ITER-like ICRH antenna (7MW) to be tested in 2006 (c) 16 new and upgraded diagnostics.

  3. Experimental studies of ITER demonstration discharges

    NASA Astrophysics Data System (ADS)

    Sips, A. C. C.; Casper, T. A.; Doyle, E. J.; Giruzzi, G.; Gribov, Y.; Hobirk, J.; Hogeweij, G. M. D.; Horton, L. D.; Hubbard, A. E.; Hutchinson, I.; Ide, S.; Isayama, A.; Imbeaux, F.; Jackson, G. L.; Kamada, Y.; Kessel, C.; Kochl, F.; Lomas, P.; Litaudon, X.; Luce, T. C.; Marmar, E.; Mattei, M.; Nunes, I.; Oyama, N.; Parail, V.; Portone, A.; Saibene, G.; Sartori, R.; Stober, J. K.; Suzuki, T.; Wolfe, S. M.; C-Mod Team; ASDEX Upgrade Team; DIII-D Team; JET EFDA Contributors

    2009-08-01

    Key parts of the ITER scenarios are determined by the capability of the proposed poloidal field (PF) coil set. They include the plasma breakdown at low loop voltage, the current rise phase, the performance during the flat top (FT) phase and a ramp down of the plasma. The ITER discharge evolution has been verified in dedicated experiments. New data are obtained from C-Mod, ASDEX Upgrade, DIII-D, JT-60U and JET. Results show that breakdown for Eaxis < 0.23-0.33 V m-1 is possible unassisted (ohmic) for large devices like JET and attainable in devices with a capability of using ECRH assist. For the current ramp up, good control of the plasma inductance is obtained using a full bore plasma shape with early X-point formation. This allows optimization of the flux usage from the PF set. Additional heating keeps li(3) < 0.85 during the ramp up to q95 = 3. A rise phase with an H-mode transition is capable of achieving li(3) < 0.7 at the start of the FT. Operation of the H-mode reference scenario at q95 ~ 3 and the hybrid scenario at q95 = 4-4.5 during the FT phase is documented, providing data for the li (3) evolution after the H-mode transition and the li (3) evolution after a back-transition to L-mode. During the ITER ramp down it is important to remain diverted and to reduce the elongation. The inductance could be kept <=1.2 during the first half of the current decay, using a slow Ip ramp down, but still consuming flux from the transformer. Alternatively, the discharges can be kept in H-mode during most of the ramp down, requiring significant amounts of additional heating.

  4. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  5. Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

    PubMed Central

    2013-01-01

    Background By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. Objective To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). Methods A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Results Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a

  6. Pavlovian to Instrumental Transfer of Control in a Human Learning Task

    PubMed Central

    Nadler, Natasha; Delgado, Mauricio R.; Delamater, Andrew R.

    2011-01-01

    Pavlovian learning tasks have been widely used as tools to understand basic cognitive and emotional processes in humans. The present studies investigated one particular task, Pavlovian-to-instrumental transfer (PIT), with human participants in an effort to examine potential cognitive and emotional effects of Pavlovian cues upon instrumentally-trained performance. In two experiments subjects first learned two separate instrumental response-outcome relationships (R1-O1, R2-O2) and then were exposed to various stimulus-outcome relationships (S1-O1, S2-O2, S3-O3, S4-) before the effects of the Pavlovian stimuli on instrumental responding were assessed during a nonreinforced test. In Experiment 1 instrumental responding was established using a positive reinforcement procedure whereas in Experiment 2 a quasi-avoidance learning task was used. In both cases the Pavlovian stimuli exerted selective control over instrumental responding, whereby S1 & S2 selectively elevated the instrumental response with which it shared an outcome. In addition, in Experiment 2, S3 exerted a nonselective transfer of control effect, whereby both responses were elevated over baseline levels. These data identify two ways, one specific and one general, in which Pavlovian processes can exert control over instrumental responding in human learning paradigms, and suggest that this method may serve as a useful tool in the study of basic cognitive and emotional processes in human learning. PMID:21534664

  7. Boundary plasma modelling for ITER

    SciTech Connect

    Braams, B.J.

    1993-01-01

    Computer programs were developed to model the effect of nonaxisymmetric magnetic perturbations upon divertor heat load, and have explored what kind of externally applied perturbations are the most effective for heat load reduction without destroying core plasma confinement. We find that a carefully tuned set of coils located about 0.3 m outside the separatrix can be used to spread the heat load over about 0.1 m perpendicular to flux surfaces at the ITER divertor plate, even at a very low level of anomalous cross-field heat transport. As such a spreading would significantly extend the permissible regime of operation for ITER, we recommend that this study be pursued at the level of detail required for engineering design. In other work under this grant we are in the process of modifying the B2 code to handle correctly a non-orthogonal geometry.

  8. Bioinspired Iterative Synthesis of Polyketides

    NASA Astrophysics Data System (ADS)

    Hong, Ran; Zheng, Kuan; Xie, Changmin

    2015-05-01

    Diverse array of biopolymers and second metabolites (particularly polyketide natural products) has been manufactured in nature through an enzymatic iterative assembly of simple building blocks. Inspired by this strategy, molecules with inherent modularity can be efficiently synthesized by repeated succession of similar reaction sequences. This privileged strategy has been widely adopted in synthetic supramolecular chemistry. Its value also has been reorganized in natural product synthesis. A brief overview of this approach is given with a particular emphasis on the total synthesis of polyol-embedded polyketides, a class of vastly diverse structures and biologically significant natural products. This viewpoint also illustrates the limits of known individual modules in terms of diastereoselectivity and enantioselectivity. More efficient and practical iterative strategies are anticipated to emerge in the future development.

  9. Bioinspired iterative synthesis of polyketides

    PubMed Central

    Zheng, Kuan; Xie, Changmin; Hong, Ran

    2015-01-01

    Diverse array of biopolymers and second metabolites (particularly polyketide natural products) has been manufactured in nature through an enzymatic iterative assembly of simple building blocks. Inspired by this strategy, molecules with inherent modularity can be efficiently synthesized by repeated succession of similar reaction sequences. This privileged strategy has been widely adopted in synthetic supramolecular chemistry. Its value also has been reorganized in natural product synthesis. A brief overview of this approach is given with a particular emphasis on the total synthesis of polyol-embedded polyketides, a class of vastly diverse structures and biologically significant natural products. This viewpoint also illustrates the limits of known individual modules in terms of diastereoselectivity and enantioselectivity. More efficient and practical iterative strategies are anticipated to emerge in the future development. PMID:26052510

  10. Cognitive control over learning: Creating, clustering and generalizing task-set structure

    PubMed Central

    Collins, Anne G.E.; Frank, Michael J.

    2013-01-01

    Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID

  11. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems

    PubMed Central

    Rückert, Elmar; d'Avella, Andrea

    2013-01-01

    A salient feature of human motor skill learning is the ability to exploit similarities across related tasks. In biological motor control, it has been hypothesized that muscle synergies, coherent activations of groups of muscles, allow for exploiting shared knowledge. Recent studies have shown that a rich set of complex motor skills can be generated by a combination of a small number of muscle synergies. In robotics, dynamic movement primitives are commonly used for motor skill learning. This machine learning approach implements a stable attractor system that facilitates learning and it can be used in high-dimensional continuous spaces. However, it does not allow for reusing shared knowledge, i.e., for each task an individual set of parameters has to be learned. We propose a novel movement primitive representation that employs parametrized basis functions, which combines the benefits of muscle synergies and dynamic movement primitives. For each task a superposition of synergies modulates a stable attractor system. This approach leads to a compact representation of multiple motor skills and at the same time enables efficient learning in high-dimensional continuous systems. The movement representation supports discrete and rhythmic movements and in particular includes the dynamic movement primitive approach as a special case. We demonstrate the feasibility of the movement representation in three multi-task learning simulated scenarios. First, the characteristics of the proposed representation are illustrated in a point-mass task. Second, in complex humanoid walking experiments, multiple walking patterns with different step heights are learned robustly and efficiently. Finally, in a multi-directional reaching task simulated with a musculoskeletal model of the human arm, we show how the proposed movement primitives can be used to learn appropriate muscle excitation patterns and to generalize effectively to new reaching skills. PMID:24146647

  12. Distinct discrimination learning strategies and their relation with spatial memory and attentional control in 4- to 14-year-olds.

    PubMed

    Schmittmann, Verena D; van der Maas, Han L J; Raijmakers, Maartje E J

    2012-04-01

    Behavioral, psychophysiological, and neuropsychological studies have revealed large developmental differences in various learning paradigms where learning from positive and negative feedback is essential. The differences are possibly due to the use of distinct strategies that may be related to spatial working memory and attentional control. In this study, strategies in performing a discrimination learning task were distinguished in a cross-sectional sample of 302 children from 4 to 14 years of age. The trial-by-trial accuracy data were analyzed with mathematical learning models. The best-fitting model revealed three learning strategies: hypothesis testing, slow abrupt learning, and nonlearning. The proportion of hypothesis-testing children increased with age. Nonlearners were present only in the youngest age group. Feature preferences for the irrelevant dimension had a detrimental effect on performance in the youngest age group. The executive functions spatial working memory and attentional control significantly predicted posterior learning strategy probabilities after controlling for age.

  13. Progress on US ITER Diagnostics

    NASA Astrophysics Data System (ADS)

    Johnson, David; Feder, Russ

    2010-11-01

    There have been significant advances in the design concepts for the 8 ITER diagnostic systems being provided by the US. Concepts for integration of the diagnostics into the port plugs have also evolved. A prerequisite for the signoff of the procurement arrangements for these each diagnostic is a Conceptual Design Review organized by the ITER Organization. US experts under contract with the USIPO have been assisting the IO to prepare for these Reviews. In addition, a design team at PPPL has been working with these experts and designers from other ITER parties to package diagnostic front-ends into the 5 US plugs. Modular diagnostic shield modules are now being considered in order to simplify the interfaces between the diagnostics within each plug. Diagnostic first wall elements are envisioned to be integral with these shield modules. This simplifies the remote handling of the diagnostics and provides flexibility for future removal of one diagnostic minimally affecting others. Front-end configurations will be presented, along with lists of issues needing resolution prior to the start of preliminary design.

  14. Differential control of learning and anxiety along the dorso-ventral axis of the dentate gyrus

    PubMed Central

    Kheirbek, Mazen A.; Drew, Liam J.; Burghardt, Nesha S.; Costantini, Daniel O.; Tannenholz, Lindsay; Ahmari, Susanne E.; Zeng, Hongkui; Fenton, André A.; Hen, René

    2013-01-01

    The dentate gyrus (DG), in addition to its role in learning and memory, is increasingly implicated in the pathophysiology of anxiety disorders. Here, we show that, dependent on their position along the dorso-ventral axis of the hippocampus, DG granule cells (GCs) control specific features of anxiety and contextual learning. Using optogenetic techniques to either elevate or decrease GC activity, we demonstrate that GCs in the dorsal DG control exploratory drive and encoding, not retrieval, of contextual fear memories. In contrast, elevating the activity of GCs in the ventral DG has no effect on contextual learning but powerfully suppresses innate anxiety. These results suggest that strategies aimed at modulating the excitability of the ventral DG may be beneficial for the treatment of anxiety disorders. PMID:23473324

  15. Non-Hebbian Learning Implementation in Light-Controlled Resistive Memory Devices

    PubMed Central

    Ungureanu, Mariana; Stoliar, Pablo; Llopis, Roger; Casanova, Fèlix; Hueso, Luis E.

    2012-01-01

    Non-Hebbian learning is often encountered in different bio-organisms. In these processes, the strength of a synapse connecting two neurons is controlled not only by the signals exchanged between the neurons, but also by an additional factor external to the synaptic structure. Here we show the implementation of non-Hebbian learning in a single solid-state resistive memory device. The output of our device is controlled not only by the applied voltages, but also by the illumination conditions under which it operates. We demonstrate that our metal/oxide/semiconductor device learns more efficiently at higher applied voltages but also when light, an external parameter, is present during the information writing steps. Conversely, memory erasing is more efficiently at higher applied voltages and in the dark. Translating neuronal activity into simple solid-state devices could provide a deeper understanding of complex brain processes and give insight into non-binary computing possibilities. PMID:23251679

  16. An Investigation into the Academic Success of Prospective Teachers in Terms of Learning Strategies, Learning Styles and the Locus of Control

    ERIC Educational Resources Information Center

    Akça, Figen

    2013-01-01

    The present research aims to investigate the relationship between the learning strategies, learning styles, the locus of control and the academic success of prospective teachers. The study group consists of 198 university students in various departments at the Uludag University Faculty of Education. Research data were collected with the Locus of…

  17. Implementation Challenges for Multivariable Control: What You Did Not Learn in School

    NASA Technical Reports Server (NTRS)

    Garg, Sanjay

    2008-01-01

    Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

  18. A controllable sensor management algorithm capable of learning

    NASA Astrophysics Data System (ADS)

    Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

    2005-03-01

    Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

  19. Space shuttle descent flight control design requirements and experiments Learned, Pt. 1 p 617-628

    NASA Technical Reports Server (NTRS)

    Kafer, G.; Wilson, D.

    1983-01-01

    Some of the lessons learned during the development of the Space Shuttle descent flight control system (FCS) are reviewed. Examples confirm the importance for requirements definition, systems level analyses, and testing. In sounding these experiences may have implication for future designs or suggest the discipline required in this engineering art.

  20. User Control and Task Authenticity for Spatial Learning in 3D Environments

    ERIC Educational Resources Information Center

    Dalgarno, Barney; Harper, Barry

    2004-01-01

    This paper describes two empirical studies which investigated the importance for spatial learning of view control and object manipulation within 3D environments. A 3D virtual chemistry laboratory was used as the research instrument. Subjects, who were university undergraduate students (34 in the first study and 80 in the second study), undertook…

  1. Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

    ERIC Educational Resources Information Center

    Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda, Jr.; Tani, Go

    2014-01-01

    Purpose: The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Method: Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys…

  2. When Prior Knowledge Interferes, Inhibitory Control Matters for Learning: The Case of Numerical Magnitude Representations

    ERIC Educational Resources Information Center

    Laski, Elida V.; Dulaney, Alana

    2015-01-01

    The present study tested the "interference hypothesis"-that learning and using more advanced representations and strategies requires the inhibition of prior, less advanced ones. Specifically, it examined the relation between inhibitory control and number line estimation performance. Experiment 1 compared the accuracy of adults' (N = 53)…

  3. Effects of Teacher Controlled Segmented-Animation Presentation in Facilitating Learning

    ERIC Educational Resources Information Center

    Mohamad Ali, Ahmad Zamzuri

    2010-01-01

    The aim of this research was to study the effectiveness of teacher controlled segmented-animation presentation on learning achievement of students with lower level of prior knowledge. Segmented-animation and continuous-animation courseware showing cellular signal transmission process were developed for the research purpose. Pre-test and post-test…

  4. Self-Reflection and the Cognitive Control of Behavior: Implications for Learning

    ERIC Educational Resources Information Center

    Marcovitch, Stuart; Jacques, Sophie; Boseovski, Janet J.; Zelazo, Philip David

    2008-01-01

    In this article, we suggest that self-reflection and self-control--studied under the rubric of "executive function" (EF)--have the potential to transform the way in which learning occurs, allowing for the relatively rapid emergence of new behaviors. We describe 2 lines of research that indicate that reflecting on a task and its affordances helps…

  5. The Relationship between Fluid Intelligence and Learning Potential: Is There an Interaction with Attentional Control?

    ERIC Educational Resources Information Center

    Filicková, Marta; Ropovik, Ivan; Bobaková, Monika; Kovalcíková, Iveta

    2015-01-01

    The main aim of the study was to explore the relationship between fluid intelligence (gf), attentional control (AC), and learning potential (LP), and to investigate the interaction effect between gf and AC on LP. The sample comprised 210 children attending the fourth grade of a standard elementary school. It was hypothesized that the extent of the…

  6. Learning Control: Sense-Making, CNC Machines, and Changes in Vocational Training for Industrial Work

    ERIC Educational Resources Information Center

    Berner, Boel

    2009-01-01

    The paper explores how novices in school-based vocational training make sense of computerized numerical control (CNC) machines. Based on two ethnographic studies in Swedish schools, one from the early 1980s and one from 2006, it analyses change and continuity in the cognitive, social, and emotional processes of learning how to become a machine…

  7. Influence of self-controlled feedback on learning a serial motor skill.

    PubMed

    Lim, Soowoen; Ali, Asif; Kim, Wonchan; Kim, Jingu; Choi, Sungmook; Radlo, Steven J

    2015-04-01

    Self-controlled feedback on a variety of tasks are well established as effective means of facilitating motor skill learning. This study assessed the effects of self-controlled feedback on the performance of a serial motor skill. The task was to learn the sequence of 18 movements that make up the Taekwondo Poomsae Taegeuk first, which is the first beginner's practice form learned in this martial art. Twenty-four novice female participants (M age=27.2 yr., SD=1.8) were divided into two groups. All participants performed 16 trials in 4 blocks of the acquisition phase and 20 hr. later, 8 trials in 2 blocks of the retention phase. The self-controlled feedback group had significantly higher performance compared to the yoked-feedback group with regard to acquisition and retention. The results of this study may contribute to the literature regarding feedback by extending the usefulness of self-controlled feedback for learning a serial skill. PMID:25914937

  8. Mixed Results from Six Large Randomized Controlled Trials of Learning Communities in Community Colleges

    ERIC Educational Resources Information Center

    Mayer, Alexander K.; Weiss, Michael J.; Visher, Mary G.; Sommo, Colleen; Rudd, Timothy; Cullinan, Dan; Weissman, Evan; Wathington, Heather D.

    2013-01-01

    This paper presents research that explores similarities and differences across six randomized controlled trials of learning communities in community colleges that were conducted by MDRC and the National Center for Postsecondary Research. Five of these studies track students' progress in the program semester and two follow-up semesters, and one…

  9. Computer-Assisted Learning in Elementary Reading: A Randomized Control Trial

    ERIC Educational Resources Information Center

    Shannon, Lisa Cassidy; Styers, Mary Koenig; Wilkerson, Stephanie Baird; Peery, Elizabeth

    2015-01-01

    This study evaluated the efficacy of Accelerated Reader, a computer-based learning program, at improving student reading. Accelerated Reader is a progress-monitoring, assessment, and practice tool that supports classroom instruction and guides independent reading. Researchers used a randomized controlled trial to evaluate the program with 344…

  10. College Student Learning from Televised Versus Conventional Classroom Lectures: A Controlled Experiment.

    ERIC Educational Resources Information Center

    Ellis, Lee; Mathis, Dan

    1985-01-01

    In a controlled experiment, students in two sections of introductory sociology were exposed either to conventional classroom lectures or to identical lectures broadcast live in an adjacent room on a television monitor. Class attendance and learning under the two modes were statistically equivalent. The findings confirm those of past studies.…

  11. The Ability of Psychological Flexibility and Job Control to Predict Learning, Job Performance, and Mental Health

    ERIC Educational Resources Information Center

    Bond, Frank W.; Flaxman, Paul E.

    2006-01-01

    This longitudinal study tested the degree to which an individual characteristic, psychological flexibility, and a work organization variable, job control, predicted ability to learn new skills at work, job performance, and mental health, amongst call center workers in the United Kingdom (N = 448). As hypothesized, results indicated that job…

  12. Beyond the Personal Learning Environment: Attachment and Control in the Classroom of the Future

    ERIC Educational Resources Information Center

    Johnson, Mark William; Sherlock, David

    2014-01-01

    The Personal Learning Environment (PLE) has been presented in a number of guises over a period of 10 years as an intervention which seeks the reorganisation of educational technology through shifting the "locus of control" of technology towards the learner. In the intervening period to the present, a number of initiatives have attempted…

  13. Predicting Learned Helplessness and Achievement: The Role of Locus on Control and Motivational Orientation.

    ERIC Educational Resources Information Center

    Early, Diane; Barrett, Marty

    This 2-year study examined the relative potency of locus of control (LOC) and motivational orientation (MO) as predictors of standardized achievement scores and learned helplessness. Also tested was the prediction that children with an extrinsic MO would be prone to adopt an external LOC over time. In the first year of the study, subjects were 158…

  14. Service Learning in Medical and Nursing Training: A Randomized Controlled Trial

    ERIC Educational Resources Information Center

    Leung, A. Y. M.; Chan, S. S. C.; Kwan, C. W.; Cheung, M. K. T.; Leung, S. S. K.; Fong, D. Y. T.

    2012-01-01

    The purpose of this study was to explore the long term effect of a service learning project on medical and nursing students' knowledge in aging and their attitudes toward older adults. A total of 124 students were recruited and then randomized to intervention group (IG) and control group (CG). A pre-and-post-intervention design measured students'…

  15. Action Control and Dispositional Hope: An Examination of Their Effect on Self-Regulated Learning

    ERIC Educational Resources Information Center

    Papantoniou, Georgia; Moraitou, Despina; Katsadima, Effie; Dinou, Magda

    2010-01-01

    Introduction: The present study examined the effect of action control (i.e., disengagement, initiative, and persistence) and dispositional hope (i.e., pathways thought, and agency thinking) on self-regulated learning strategy use (i.e., cognitive, metacognitive, and resource management) and course achievement. Method: A total of 275 undergraduate…

  16. Self-Controlled Feedback in 10-Year-Old Children: Higher Feedback Frequencies Enhance Learning

    ERIC Educational Resources Information Center

    Chiviacowsky, Suzete; Wulf, Gabriele; de Medeiros, Franklin Laroque; Kaefer, Angelica; Wally, Raquel

    2008-01-01

    The purpose of the present study was to examine whether learning in 10-year-old children--that is, the age group for which the Chiviacowsky et al. (2006) study found benefits of self-controlled knowledge of results (KR)--would differ depending on the frequency of feedback they chose. The authors surmised that a relatively high feedback frequency…

  17. Learning Benefits of Self-Controlled Knowledge of Results in 10-Year-Old Children

    ERIC Educational Resources Information Center

    Chiviacowsky, Suzete; Wulf, Gabriele; Laroque de Medeiros, Franklin; Kaefer, Angelica; Tani, Go

    2008-01-01

    The purpose of the present study was to examine whether the learning benefits of self-controlled knowledge of results (KR) would generalize to children. Specifically, the authors chose 10-year-old children representative of late childhood. The authors used a task that required the children to toss beanbags at a target. One group received KR…

  18. Preparing Content-Rich Learning Environments with VPython and Excel, Controlled by Visual Basic for Applications

    ERIC Educational Resources Information Center

    Prayaga, Chandra

    2008-01-01

    A simple interface between VPython and Microsoft (MS) Office products such as Word and Excel, controlled by Visual Basic for Applications, is described. The interface allows the preparation of content-rich, interactive learning environments by taking advantage of the three-dimensional (3D) visualization capabilities of VPython and the GUI…

  19. Learning Experiment: Determine Effectiveness of Controlling Environmental Distractions at the Student Level.

    ERIC Educational Resources Information Center

    Sumter, Paul Edward

    The purpose of this study was to see if learning could be improved by controlling the environment at the individual student's level. A pretest, post-test, random choice design was chosen to obtain data from over 900 subjects of technical-vocational schools, area community colleges, and high schools of Iowa, with emphasis on grades 11 and 12 and…

  20. Inhibitory Control in Mathematical Thinking, Learning and Problem Solving: A Survey

    ERIC Educational Resources Information Center

    Van Dooren, Wim; Inglis, Matthew

    2015-01-01

    Inhibitory control--the ability to ignore salient but unhelpful stimuli and responses--seems to be important for learning mathematics. For instance there is now robust evidence that performance on classic measures of inhibition, such as the Stroop Task, correlate with school-level mathematics achievement. At the same time, a great deal of…